1. A high altitude parabolic detection method, the method comprising:

sequentially reading two adjacent images to be detected from a plurality of frames of images to be detected according to the time sequence of image acquisition, and generating optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model;

determining optical flow images to be detected with falling objects from the optical flow images, and determining the position coordinates of the central points of the falling objects in the optical flow images to be detected;

determining the motion trail of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected;

and executing high-altitude parabolic detection processing based on the motion trail.

2. The method of claim 1, wherein said determining from said optical flow images a to-be-measured optical flow image in which a dropped object is present comprises:

for any optical flow image, generating a binary image corresponding to the optical flow image; the binary image comprises a foreground moving object with a first pixel value and a background non-moving object with a second pixel value;

and determining any optical flow image as the optical flow image to be detected with the throwing object in response to the pixel proportion of the foreground moving object in the binary image being less than or equal to a preset proportion threshold value.

3. The method according to claim 2, wherein the generating a binary image corresponding to any one of the optical flow images comprises:

carrying out single-channel gray scale conversion processing on any optical flow image to obtain a single-channel gray scale image corresponding to any optical flow image;

carrying out normalization processing on the single-channel gray image to obtain a normalized gray image corresponding to any optical flow image;

and carrying out binarization processing on the normalized gray level image to obtain the binary image corresponding to any optical flow image.

4. The method of claim 3, wherein said determining coordinates of a center point location of said dropped object in each optical flow image under test comprises:

for any optical flow image to be detected, acquiring an initial position coordinate set of the foreground moving object in the binary image, and classifying the foreground moving object in the optical flow image to be detected based on a preset clustering algorithm and the initial position coordinate set to obtain at least one moving object and a coordinate subset corresponding to each moving object;

for any moving object, calculating a coordinate average value corresponding to the coordinate subset of the moving object, and determining the coordinate average value as the central point position coordinate of the moving object;

and determining a target pixel value at the position coordinate of the central point of any moving object in the normalized gray-scale image corresponding to any to-be-detected flow image, determining the moving object with the maximum target pixel value as the falling object, and determining the position coordinate of the central point corresponding to the moving object with the maximum target pixel value as the position coordinate of the central point of the falling object.

5. The method according to claim 3, wherein before determining the motion trajectory of the falling object according to the position coordinates of the center point of the falling object in each optical flow image to be measured, the method further comprises:

determining a starting optical flow image to be detected to an ending optical flow image to be detected of a parabolic event corresponding to the falling object from the optical flow images to be detected;

the determining the motion track of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected comprises the following steps:

and carrying out interpolation processing on the coordinates of the central point position of the falling object in each optical flow image to be detected in the parabolic event according to the time sequence to obtain the motion track of the falling object.

6. The method according to claim 5, characterized in that said optical flow image under test comprises at least a first image and a second image, the first image being the optical flow image under test for the current end of a first parabolic event; the first image and the second image are two continuous frames of optical flow images to be detected;

the determining, from the optical flow images to be measured, an initial optical flow image to be measured to an end optical flow image to be measured of the parabolic event corresponding to the falling object includes:

calculating a frame interval value between the second image and the first image;

in response to the frame interval value being less than or equal to a preset interval threshold, updating the second image to the ending optical flow to be measured image of the first parabolic event;

and determining the second image as a starting optical flow image to be tested of a second parabolic event in response to the frame interval value being greater than the preset interval threshold.

7. The method of claim 5 or 6, wherein the performing high altitude parabolic detection processing based on the motion trajectory comprises:

performing linear fitting processing on the motion track of the falling object to obtain a fitted straight line corresponding to the motion track, and determining an included angle between the fitted straight line and the vertical direction;

determining a central point position pixel value from the corresponding normalized gray level image based on the central point position coordinates of the falling object in each optical flow image to be detected in the parabolic event, and performing accumulation processing on the central point position pixel values to obtain a pixel accumulated value;

performing difference value operation of vertical coordinates on the position coordinate of the center point of the falling object in the initial optical flow image to be detected and the position coordinate of the center point of the falling object in the ending optical flow image to be detected in the parabolic event to obtain a coordinate difference value;

and executing the high-altitude parabolic detection processing based on the included angle between the fitted straight line and the vertical direction, the pixel accumulated value and the coordinate difference value.

8. The method according to claim 7, wherein the performing the high altitude parabolic detection processing based on the angle between the fitted straight line and the vertical direction, the pixel accumulated value, and the coordinate difference value comprises:

and determining that the falling object is a high-altitude falling object and the corresponding parabolic event is a high-altitude parabolic event in response to that the included angle between the fitted straight line and the vertical direction is smaller than a preset angle threshold, the pixel accumulated value is smaller than a preset pixel threshold and the coordinate difference value is larger than a preset height threshold.

9. The method according to any one of claims 1 to 8, further comprising:

acquiring an initial image, and determining a target detection area from the initial image based on a preset polygon outline;

and generating a minimum detection frame based on the target detection area, and performing image segmentation processing on the initial image based on the minimum detection frame to obtain the image to be detected.

10. A high altitude parabolic detection device, characterized in that it comprises:

the reading unit is configured to read two adjacent images to be detected from the multiple frames of images to be detected in sequence according to the time sequence of image acquisition;

the generating unit is configured to generate optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model;

a determination unit configured to determine optical flow images to be measured in which a dropped object exists from the optical flow images, and determine position coordinates of a center point of the dropped object in each optical flow image to be measured;

the determining unit is further configured to determine a motion track of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected;

a processing unit configured to perform a high altitude parabolic detection process based on the motion trajectory.

11. A high altitude parabolic detection apparatus comprising a processor, a memory storing instructions executable by the processor which, when executed by the processor, implement a method as claimed in any one of claims 1 to 9.

12. A computer-readable storage medium, having a program stored thereon, for use in a high altitude parabolic detection apparatus, wherein the program, when executed by a processor, implements a method as claimed in any one of claims 1 to 9.

Background

In recent years, with the increase of the urbanization rate, more and more high-rise buildings and more attention is paid to the danger caused by high-altitude parabolic movement. High-altitude parabolic detection is used as an important technology in an intelligent video monitoring system, and by analyzing a building floor monitoring video, a falling object is detected, so that defense is distributed on the floor of a high-rise building in a city, and a possible high-altitude parabolic event is alarmed in real time.

Disclosure of Invention

The disclosed embodiments provide a high altitude parabolic detection method, apparatus, device, and computer storage medium.

The technical scheme of the disclosure is realized as follows:

the embodiment of the disclosure provides a high-altitude parabolic detection method, which comprises the following steps:

sequentially reading two adjacent images to be detected from a plurality of frames of images to be detected according to the time sequence of image acquisition, and generating optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model; determining optical flow images to be detected with falling objects from the optical flow images, and determining the position coordinates of the central points of the falling objects in the optical flow images to be detected; determining the motion trail of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected; and executing high-altitude parabolic detection processing based on the motion trail.

Therefore, on one hand, a dense light flow graph is generated by adopting the light flow model constructed based on deep learning to realize detection of moving objects, so that the robustness is good, the precision is high, the time consumption is shorter, and the noise is less; on the other hand, on the basis of moving object detection by using a preset optical flow model, based on a robust single-frame and multi-frame post-processing method, single frames with interferents are filtered, single-frame images with falling objects are found out, and after single-frame false detection is removed, track restoration and high-altitude parabolic event detection are carried out by combining falling object position information on multiple frames.

In the above method, the determining, from the optical flow images, an optical flow image to be measured in which the dropped object exists includes:

for any optical flow image, generating a binary image corresponding to the optical flow image; the binary image comprises a foreground moving object with a first pixel value and a background non-moving object with a second pixel value; and determining any optical flow image as the optical flow image to be detected with the throwing object in response to the pixel proportion of the foreground moving object in the binary image being less than or equal to a preset proportion threshold value.

In the above method, the generating a binary image corresponding to any one of the optical flow images includes:

for any optical flow image, carrying out single-channel gray scale conversion processing on any optical flow image to obtain a single-channel gray scale image corresponding to any optical flow image; carrying out normalization processing on the single-channel gray image to obtain a normalized gray image corresponding to any optical flow image; and carrying out binarization processing on the normalized gray level image to obtain the binary image corresponding to any optical flow image.

In this way, the optical flow image with the throwing object can be determined by performing single-frame binarization processing on the current optical flow image and filtering the optical flow image with overlarge noise or abnormal noise based on the pixel number of the foreground moving object in the binarization image, so that the precision and the speed of the high-altitude detection processing are improved.

In the above method, the determining the position coordinates of the center point of the falling object in each optical flow image to be detected includes:

for any optical flow image to be detected, acquiring an initial position coordinate set of the foreground moving object in the binary image;

classifying the foreground moving object in any to-be-detected optical flow image based on a preset clustering algorithm and the initial coordinate set to obtain at least one moving object and a coordinate subset corresponding to each moving object; for any moving object, calculating a coordinate average value corresponding to the coordinate subset of the moving object, and determining the coordinate average value as the central point position coordinate of the moving object; determining a target pixel value at the position coordinate of the central point of any moving object in any normalized gray scale image corresponding to any to-be-detected flow image; and determining the moving object with the maximum target pixel value as the falling object, and determining the central point position coordinate corresponding to the moving object with the maximum target pixel value as the central point position coordinate of the falling object.

Therefore, through the position clustering algorithm, the cluster corresponding to the maximum optical flow amplitude is taken as the falling object position, interference objects in the image can be effectively filtered, and the high-altitude parabolic detection precision is improved.

In the above method, before determining the motion trajectory of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected, the method further includes: determining a starting optical flow image to be detected to an ending optical flow image to be detected of a parabolic event corresponding to the falling object from the optical flow images to be detected;

the determining the motion track of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected comprises the following steps: and carrying out interpolation processing on the coordinates of the central point position of the falling object in each optical flow image to be detected in the parabolic event according to the time sequence to obtain the motion track of the falling object.

In the method, the optical flow image to be measured at least comprises a first image and a second image, and the first image is a current ending optical flow image to be measured of a first parabolic event; the first image and the second image are two continuous frames of optical flow images to be detected;

the determining, from the optical flow images to be measured, an initial optical flow image to be measured to an end optical flow image to be measured of the parabolic event corresponding to the falling object includes: calculating a frame interval value between the second image and the first image; in response to the frame interval value being less than or equal to a preset interval threshold, updating the second image to the ending optical flow to be measured image of the first parabolic event; and determining the second image as a starting optical flow image to be tested of a second parabolic event in response to the frame interval value being greater than the preset interval threshold.

Therefore, the starting frame and the ending frame of different motion events to be detected can be determined based on the frame interval difference value, so that the different motion events to be detected can be accurately divided, and the precision of high-altitude parabolic detection processing is improved.

In the above method, the performing high altitude parabolic detection processing based on the motion trajectory includes: performing linear fitting processing on the motion track of the falling object to obtain a fitted straight line corresponding to the motion track, and determining an included angle between the fitted straight line and the vertical direction; determining a central point position pixel value from the corresponding normalized gray level image based on the central point position coordinates of the falling object in each optical flow image to be detected in the parabolic event, and performing accumulation processing on the central point position pixel values to obtain a pixel accumulated value; performing difference value operation of vertical coordinates on the position coordinate of the center point of the falling object in the initial optical flow image to be detected and the position coordinate of the center point of the falling object in the ending optical flow image to be detected in the parabolic event to obtain a coordinate difference value; and executing the high-altitude parabolic detection processing based on the included angle between the fitted straight line and the vertical direction, the pixel accumulated value and the coordinate difference value.

In the above method, the executing the high altitude parabolic detection processing based on the included angle between the fitted straight line and the vertical direction, the pixel accumulated value, and the coordinate difference value includes: and determining that the falling object is a high-altitude falling object and the corresponding parabolic event is a high-altitude parabolic event in response to that the included angle between the fitted straight line and the vertical direction is smaller than a preset angle threshold, the pixel accumulated value is smaller than a preset pixel threshold and the coordinate difference value is larger than a preset height threshold.

Therefore, the event detection method can be used for accurately judging the high-altitude parabolic event based on the motion track after the complete track reduction is carried out by combining the detection information of the falling object on the multiple frames.

In the above method, the high altitude parabolic detection method further includes: acquiring an initial image, and determining a target detection area from the initial image based on a preset polygon outline; and generating a minimum detection frame based on the target detection area, and performing image segmentation processing on the initial image based on the minimum detection frame to obtain the image to be detected.

Therefore, the negative influence on the false detection of the falling object can be reduced, the task amount of image post-processing is reduced, and the high-altitude parabolic detection processing speed is further increased.

The disclosed embodiment provides a high altitude parabolic detection device, including:

the reading unit is configured to read two adjacent images to be detected from the multiple frames of images to be detected in sequence according to the time sequence of image acquisition;

the generating unit is configured to generate optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model;

a determination unit configured to determine optical flow images to be measured in which a dropped object exists from the optical flow images, and determine position coordinates of a center point of the dropped object in each optical flow image to be measured;

the determining unit is further configured to determine a motion track of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected;

a processing unit configured to perform a high altitude parabolic detection process based on the motion trajectory.

The embodiment of the present disclosure provides a high altitude parabolic detection device, which includes a processor, and a memory storing executable instructions of the processor, and when the instructions are executed by the processor, the high altitude parabolic detection device implements the high altitude parabolic detection method as described above.

The embodiment of the disclosure provides a computer readable storage medium, which stores a program, and is applied to a high altitude parabolic detection device, wherein the program is executed by a processor to realize the high altitude parabolic detection method.

According to the technical scheme provided by the embodiment of the disclosure, the high-altitude parabolic detection equipment can sequentially read two adjacent images to be detected from a plurality of frames of images to be detected according to the time sequence of image acquisition, and generates optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model; determining to-be-detected optical flow images with the falling objects in the optical flow images, and determining the position coordinates of the central points of the falling objects in the to-be-detected optical flow images; determining the motion trail of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected; and executing high-altitude parabolic detection processing based on the motion trail. Therefore, on one hand, the dense light flow graph is generated by adopting the light flow model constructed based on the deep learning so as to realize the detection of the moving object, the robustness is good, the precision is high, the time consumption is shorter, and the noise is less; on the other hand, on the basis of detecting moving objects by using a preset optical flow model, the method also provides a robust single-frame and multi-frame post-processing method, the single frame with the interferent is filtered, the single-frame image with the falling object is found out, after the single-frame false detection is removed, the track restoration and the high-altitude parabolic event detection are carried out by combining the position information of the falling object on the multi-frame, and the efficiency and the precision of the high-altitude parabolic detection are further improved.

Drawings

Fig. 1 is a schematic flow chart of an implementation of a high altitude parabolic detection method according to an embodiment of the present disclosure;

fig. 2 is a schematic flow chart illustrating an implementation process of a high altitude parabolic detection method according to an embodiment of the present disclosure;

fig. 3 is a schematic diagram of a motion trajectory of a throwing object according to an embodiment of the present disclosure;

fig. 4 is a schematic flow chart illustrating an implementation of a high altitude parabolic detection method according to an embodiment of the present disclosure;

fig. 5 is a schematic flow chart illustrating an implementation of a high altitude parabolic detection method according to an embodiment of the present disclosure;

fig. 6 is a schematic flow chart illustrating an implementation of a high altitude parabolic detection method according to an embodiment of the present disclosure;

fig. 7 is a schematic flow chart illustrating an implementation process of a high altitude parabolic detection method according to an embodiment of the present disclosure;

fig. 8 is a schematic flow chart illustrating an implementation of a high altitude parabolic detection method according to an embodiment of the present disclosure;

fig. 9 is a schematic flow chart eight of an implementation process of the high altitude parabolic detection method according to the embodiment of the present disclosure;

fig. 10 is a schematic flow chart illustrating an implementation process of a high altitude parabolic detection method according to an embodiment of the present disclosure;

fig. 11 is a scene schematic diagram of a high altitude parabolic detection method according to an embodiment of the disclosure;

fig. 12 is a schematic structural diagram of a high altitude parabolic detection apparatus according to an embodiment of the present disclosure;

fig. 13 is a schematic structural diagram of a high altitude parabolic detection apparatus according to an embodiment of the present disclosure.

Detailed Description

For the purpose of making the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be described in further detail with reference to the accompanying drawings, the described embodiments should not be construed as limiting the present disclosure, and all other embodiments obtained by a person of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present disclosure.

In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

In the following description, references to the terms "first \ second \ third" are only to distinguish similar objects and do not denote a particular order, but rather the terms "first \ second \ third" are used to interchange specific orders or sequences, where permissible, so that the disclosed embodiments described herein can be practiced in other than the order shown or described herein.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of the disclosure only and is not intended to be limiting of the disclosure.

In recent years, with the increase of the urbanization rate, more and more high-rise buildings and more attention is paid to the danger caused by high-altitude parabolic movement. High-altitude parabolic detection is used as an important technology in an intelligent video monitoring system, and by analyzing a building floor monitoring video, a falling object is detected, so that defense is distributed on the floor of a high-rise building in a city, and a possible high-altitude parabolic event is alarmed in real time.

Specifically, the high-altitude parabolic detection depends on the detection of a moving object in an image, and the detection of the moving object is often performed by using an inter-frame difference method or a background difference method in the related art.

The interframe difference method is based on the following principle: when moving objects exist in a video, the adjacent frames (or the adjacent three frames) have difference in gray level, and the absolute value of the gray level difference of the two frames of images is obtained, then the static objects are all represented as 0 on the difference image, and the moving objects, particularly the outlines of the moving objects, can be judged as moving objects due to the gray level change of non-0, when the absolute value exceeds a certain threshold value, so that the detection function of the objects is realized, namely, the adjacent inter-frame difference method directly performs difference operation on the adjacent two frames of images, and the absolute value of the difference operation is obtained to form the moving objects.

Although the interframe difference method can obtain the outline of the moving target, the method is simple and the operation complexity is small, the method has the defects that a camera needs to be fixed, the robust phase difference is low and the precision is low, and is generally only suitable for simple real-time motion detection.

The background difference method is based on the basic principle that a current frame in an image sequence and a determined or real-time acquired background reference model (background image) are subtracted to find differences, and an area with pixel difference exceeding a certain threshold value with the background image is calculated to be determined as a motion area, so that the characteristics of motion position, contour, size and the like are determined.

The background difference method can obtain the whole area of the moving target, and has the advantages of high speed, accuracy, poor robustness, fixed camera, difficult utilization of interframe information and image post-processing, and particularly, weak illumination or great influence on rainy and snowy weather.

In order to overcome the defects of the interframe difference method and the background difference method, the related technology further provides moving object detection based on an optical flow method, such as an algorithm applying an optical flow constraint equation and a traditional (Lucas Kanade, KL) algorithm, wherein the optical flow method mainly uses the change of pixels in an image sequence in a time domain and the correlation between adjacent frames to calculate the motion information of an object between the adjacent frames according to the corresponding relation between the previous frame and the current frame.

Although the method can detect the whole area of a moving target, is suitable for the static and moving conditions of a camera, has good robustness and high precision, most optical flow calculation methods have huge calculation amount and complex structure, so that the calculation time is high, and the speed and the precision are difficult to ensure at the same time.

Therefore, under the condition of massive video data, the traditional high-altitude parabolic detection method is difficult to meet the high requirements of high-altitude parabolic detection on speed and precision. In view of this, how to ensure the high requirement of the high altitude parabolic detection on speed and precision is an urgent problem to be solved, which is the content to be discussed in the embodiments of the present disclosure, the following embodiments will be described in detail below.

The embodiment of the disclosure provides a high-altitude parabolic detection method, a high-altitude parabolic detection device, high-altitude parabolic detection equipment and a computer storage medium, on one hand, a dense light flow graph is generated by adopting a light flow model constructed based on deep learning to realize detection of moving objects, and the method has the advantages of good robustness, high precision, shorter time consumption and less noise; on the other hand, on the basis of detecting moving objects by using a preset optical flow model, the method also provides a robust single-frame and multi-frame post-processing method, the single frame with the interferent is filtered, the single-frame image with the falling object is found out, after the single-frame false detection is removed, the track restoration and the detection of the high-altitude parabolic event are carried out by combining the position information of the falling object on the multiple frames, and the efficiency and the precision of the high-altitude parabolic detection are further improved.

The high-altitude parabolic detection method provided by the embodiment of the disclosure is applied to high-altitude parabolic detection equipment. The following describes an exemplary application of the high altitude parabolic detection device provided by the embodiment of the present disclosure, which may be implemented as a mobile phone terminal, a notebook computer, a tablet computer, a desktop computer, a server, various industrial devices, and the like.

In the following, the technical solutions in the embodiments of the present disclosure will be clearly and completely described with reference to the drawings in the embodiments of the present disclosure.

An embodiment of the present disclosure provides a high altitude parabolic detection method, and fig. 1 is a schematic flow chart illustrating an implementation process of the high altitude parabolic detection method provided by the embodiment of the present disclosure, as shown in fig. 1, in the embodiment of the present disclosure, a method for performing high altitude parabolic detection may include the following steps:

s100, sequentially reading two adjacent images to be detected from the multiple frames of images to be detected according to the time sequence of image acquisition, and generating optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model.

It should be understood that, in the embodiment of the present disclosure, the high-altitude parabolic detection processing may be performed on a historical event, or may be performed in real time on a currently occurring event.

In some embodiments, the image to be detected refers to an image which needs to be subjected to moving object detection. Wherein the image may be acquired in real time or stored locally.

In some embodiments, the image to be measured may be an RGB color image, a grayscale image, or other sensor image data (e.g., infrared image) based on image capturing and monitoring devices with different configurations.

The high-altitude parabolic detection equipment can be configured with image acquisition monitoring equipment such as a camera, and an image to be detected can be acquired in real time through the camera; or, a section of video collected by the camera history is stored locally, and the video stream can be read and analyzed locally to obtain the image to be detected.

Fig. 2 is a schematic diagram of a second implementation flow of the high altitude parabolic detection method according to the embodiment of the present disclosure, and as shown in fig. 2, the method for acquiring the image to be detected may include the following steps:

s101, acquiring an initial image, and determining a target detection area from the initial image based on a preset polygon outline.

It should be understood that for any image capturing and monitoring device, the range of images that can be captured may include the target monitoring object (such as a building) and greening or fixed devices under the building, such as lamp poles and numbers. Particularly, greening facilities such as trees and the like bring certain influence on the detection result of a moving object, the influence can be shown as that fallen leaves are easily judged to be high-altitude fallen objects, an additional processing task is brought to image post-processing, and the high-altitude parabolic detection processing speed is reduced.

Therefore, in the embodiment of the present disclosure, the monitoring picture of each image capturing and monitoring device may be analyzed, and a region without a blocking object is calibrated as a monitoring region of a target monitoring object (such as a building), so that when performing subsequent image analysis, only the monitoring region is analyzed, image contents of other non-monitoring regions are ignored, and the high-altitude parabolic detection processing speed is increased.

In some embodiments, the initial image refers to an image within a monitoring range acquired in real time by an image acquisition monitoring device, or a video within a monitoring range stored in a local history.

In the embodiment of the present disclosure, a polygon contour C, that is, a preset polygon contour, may be determined manually, and then an interested region R is marked from an initial image by the preset polygon contour as a target detection region, so as to omit other image contents with interference in the initial image.

Here, the high altitude parabolic detection device may perform binarization processing on the initial image based on a preset polygon profile, set image pixels of a target detection region corresponding to the preset polygon profile to 1 first, set image pixels outside the target detection region to 0, and then perform pixel dot multiplication on the binarized image and the initial image so that the image of the target detection region retains the original pixel values, and the image pixels outside the region are all 0, so as to determine the target detection region existing in the initial image.

Therefore, except for the selected target falling object area, the pixels of other areas are all 0, and the obtained target detection area can cover a target monitoring object (such as a building) to the maximum extent, namely the potential motion track of the falling object to the maximum extent, and meanwhile, the influence of trees, sky, lamp posts and other interferents is avoided.

And S102, generating a minimum detection frame based on the target detection area, and performing image segmentation processing on the initial image based on the minimum detection frame to obtain an image to be detected.

It will be appreciated that in order to maximize the potential motion trajectory of the falling object, the actual target falling object may be an irregular polygon, and then the target falling object region needs to be regularized before the model is input. And generating a Bounding box minimum detection frame corresponding to the target falling object region.

The coordinate of the edge position of the target object falling object area can be taken to generate a minimum detection frame. May be to take the leftmost edge coordinate x of the target fall object region₁Rightmost edge coordinate x₂The uppermost edge coordinate y₁And the lowest edge coordinate y₂And then generates a minimum bounding box (x)₁，y₁，x₂，y₂)。

In an implementation manner of the embodiment of the present disclosure, an image segmentation process, that is, image frame clipping may be performed on an initial image based on the minimum detection frame, so as to obtain an image to be detected.

It can be seen that in the embodiment of the present disclosure, negative effects on the false detection of a falling object can be reduced based on S101a-S101b, the amount of image post-processing tasks is reduced, and the speed of high-altitude parabolic detection processing is further increased.

In the embodiment of the disclosure, the preset optical flow model is a model which is based on a deep learning technology, is constructed and trained to have high precision and high inference speed through a convolutional neural network and a cyclic neural network. The input of the optical flow model is two adjacent frames of images to be detected, and the output of the optical flow model is an optical flow graph capable of reflecting the moving speed and direction of a moving object, so that the detection of the moving object in the dropped image is realized.

In some embodiments, the optical flow image is an image reflecting the moving speed and moving direction of a moving object, and in order to determine the moving object possibly existing in the video stream, the optical flow image can be generated based on adjacent frames in the video stream, and the optical flow image can reflect the movement of the moving object from the previous frame image to the point of the second frame image. It can be seen that all moving objects present can be found based on the optical flow graph.

In an implementation manner of the embodiment of the present disclosure, each two adjacent frames of images to be measured in the multi-frame images to be measured obtained through the processing in S101a-S101b may be sequentially configured into an image sample pair, such as an image sample pair (I) according to the time sequence of image acquisition_k,I_k+1) Then, inputting the image sample pair into a preset optical flow model to obtain a dense optical flow diagram for reflecting the moving object, namely, generating optical flow images corresponding to two adjacent images to be detected through the preset optical flow model.

Here, the current optical flow image can be expressed using the following formula:

f_k＝(u_k,v_k) (1)

wherein, in the formula (1), u_kCharacterizing the speed of movement, v, of a moving object in the vertical direction_kRepresenting the motion speed in the horizontal direction, and k represents the frame ordering value of the image, such as the k frame optical flow image.

In some embodiments, if the high-altitude parabolic detection processing is performed on the event which occurs in the history, S101a-S101b may be performed on each frame of image in the locally stored history video to obtain the image to be detected corresponding to each frame of image, then the image to be detected obtained after image segmentation is read in batches, an image sample pair is sequentially constructed on the basis of image acquisition time for two adjacent frames of images to be detected, and a preset optical flow model is input to perform moving object detection, so as to generate an optical flow image corresponding to each two adjacent images to be detected. Correspondingly, if the high-altitude parabolic detection processing is carried out on the current happening event, S101a-S101b are executed in real time according to the collecting time to obtain the images to be detected, and two adjacent frames of images to be detected are input into a preset optical flow model in real time to carry out moving object detection so as to generate optical flow images corresponding to two adjacent images to be detected.

S110, determining the optical flow images to be detected with the falling objects from the optical flow images, and determining the position coordinates of the central points of the falling objects in the optical flow images to be detected.

It can be understood that there are many scenes of high altitude parabolic, and different natural conditions such as day, night, rain, etc. affect the detection accuracy of the dropped object, and some natural objects such as swinging leaves, flying birds, moving people, etc. can affect the accurate detection of the dropped object. Therefore, in order to overcome detection errors caused by other non-high-altitude falling moving objects to high-altitude parabolic detection in the detection process, in the embodiment of the disclosure, after optical flow images corresponding to two adjacent images to be detected are generated based on a preset optical flow model, a single-frame post-processing method can be used for filtering interference images or abnormal images of the non-high-altitude falling objects, so as to obtain images with real falling objects and less noise, and reduce the false detection rate of the high-altitude parabolic detection.

It should be understood that if the types of moving objects in the current optical flow image are too many, which indicates that the noise in the image is too much, or the motion response strength of the moving objects in the current optical flow image is too large, which indicates that the frame image is abnormal, for example, a bird flies through the lens, and such an image has a large negative influence on false detection caused by falling object detection, so that the optical flow image has a small role in performing high-altitude falling detection processing and slows down the high-altitude detection processing.

Therefore, in the embodiment of the present disclosure, the obtained optical flow images may be further analyzed and processed, and the current optical flow images or abnormal optical flow images determined as having excessive noise and excessive moving object types may be filtered to screen out the optical flow image to be measured when there is a falling object.

In an implementation manner of the embodiment of the present disclosure, a single-frame binarization method may be adopted to determine whether a true dropped object exists in a current optical flow image.

In some embodiments, for any determined optical flow image in which the falling object exists, the position coordinates of the center point of the falling object in any optical flow image to be detected can be determined continuously. A position clustering method can be introduced to determine the position coordinates of the central point of the falling object.

According to the method of S100-S110, the optical flow images collected in real time can be subjected to repeated processing of the processes of generating the optical flow images, determining whether the falling object exists in the corresponding optical flow images, determining the position coordinates of the central point of the falling object in the optical flow images to be detected and the like, so that a plurality of frames of optical flow images to be detected and the position coordinates of the central point of the falling object in each optical flow image to be detected can be obtained.

And S120, determining the motion trail of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected.

And S130, executing high-altitude parabolic detection processing based on the motion trail.

In the embodiment of the disclosure, the trajectory of the falling object may be restored based on a multi-frame post-processing method, wherein the trajectory of the movement of the falling object may be restored based on the position coordinates of the center point of the falling object in each optical flow image to be detected.

The position coordinates of the central point of the falling object in each optical flow image to be detected are independent and single, so that the position coordinates of the central point of the falling object in each optical flow image to be detected can be interpolated according to the generation time sequence of each optical flow image to be detected, the position coordinates of the central point of the falling object in each optical flow image to be detected are mapped on a plane image, and the position coordinates of the central point of the falling object are connected to form a motion track corresponding to the falling object.

Exemplarily, fig. 3 is a schematic diagram of a motion trajectory of a throwing object according to an embodiment of the present disclosure, where the motion trajectory is formed by coordinates of a plurality of central points of the throwing object in a plurality of continuous frames of optical flow images to be measured.

It can be understood that, since some buildings, trees, or the like may be blocked during the dropping process of the object, the determined multiple frames of to-be-measured images in which the dropped object exists are not frame-sequential, but based on the frame density, the motion trajectory of the dropped object may also be restored based on the position coordinates of the central point of the dropped object in most of the to-be-measured optical flow images.

In an implementation manner of the embodiment of the disclosure, after the motion trajectory corresponding to the falling object is restored, a trajectory straight line fitting method may be introduced to perform high-altitude parabolic event detection on the falling object, so as to further determine whether the motion event corresponding to the falling object belongs to a high-altitude parabolic event. The high-altitude parabolic event detection can be performed on the falling object by combining information such as an included angle between the straight-line fitted track and the vertical direction (slope of the straight-line fitted track), the falling height of the object and the like.

For example, the event that a person lifts an apple by hand in front of a camera is a motion event of throwing an object, but the event is not a high-altitude parabolic event in nature; and the sport event of a person standing in the eight floors of the building and throwing the apples down to the ground is regarded as a high-altitude parabolic event.

The embodiment of the disclosure provides a high-altitude parabolic detection method, wherein high-altitude parabolic detection equipment sequentially reads two adjacent images to be detected from a plurality of frames of images to be detected according to the time sequence of image acquisition and generates optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model; determining to-be-detected optical flow images with the falling objects in the optical flow images, and determining the position coordinates of the central points of the falling objects in the to-be-detected optical flow images; determining the motion trail of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected; and executing high-altitude parabolic detection processing based on the motion trail. Therefore, on one hand, the dense light flow graph is generated by adopting the light flow model constructed based on the deep learning so as to realize the detection of the moving object, the robustness is good, the precision is high, the time consumption is shorter, and the noise is less; on the other hand, on the basis of detecting moving objects by using a preset optical flow model, the method also provides a robust single-frame and multi-frame post-processing method, the single frame with the interferent is filtered, the single-frame image with the falling object is found out, after the single-frame false detection is removed, the track restoration and the high-altitude parabolic event detection are carried out by combining the position information of the falling object on the multi-frame, and the efficiency and the precision of the high-altitude parabolic detection are further improved.

Based on the foregoing embodiment, in yet another implementation manner of the embodiment of the present disclosure, as shown in fig. 4, fig. 4 is a schematic flow chart of an implementation process of a high altitude parabolic detection method provided by the embodiment of the present disclosure, and as shown in fig. 4, the method for determining, by a high altitude parabolic detection device, that there is an optical flow image to be detected of a dropped object from the optical flow image further includes the following steps:

s111, generating a binary image corresponding to any optical flow image for any optical flow image; the binary image comprises a foreground moving object with a first pixel value and a background non-moving object with a second pixel value.

In the embodiment of the disclosure, after the optical flow images corresponding to two adjacent images to be detected are generated based on the preset optical flow model, whether a true dropped object exists in the current optical flow image may be determined by using a single-frame binarization method, so as to determine the optical flow image to be detected in which the dropped object exists.

For any optical flow image, the optical flow image may be converted into a binary image, that is, a grayscale image in which the image only includes the first pixel value and the second pixel value. For example, the binary image corresponding to the optical flow image includes a pixel value 0 and a pixel value 255.

In an implementation manner of the embodiment of the present disclosure, in order to distinguish a moving object from a non-moving object in an image, a region in which a pixel value in the image is greater than or equal to a critical pixel gray scale value is determined as a region in which the moving object exists, and a pixel value of the region in a binary image is set as a first pixel value, whereas a region in which a pixel value in the image is less than the critical pixel gray scale value is determined as a region in which the non-moving object exists, and a pixel value of the region in the binary image is set as a second pixel value, so that a binary image corresponding to any one of the optical flow images is obtained.

Here, the first pixel value in the binary image may be 255, which represents a foreground moving object, and the second pixel value may be 0, which represents a background non-pixel object.

Fig. 5 is a schematic flow chart of a fourth implementation flow of the high altitude parabolic detection method provided in the embodiment of the present disclosure, and as shown in fig. 5, the method for generating a binary image corresponding to any one of the optical flow images may include the following steps:

and S111a, performing single-channel gray scale conversion processing on any optical flow image to obtain a single-channel gray scale image corresponding to any optical flow image.

And S111b, carrying out normalization processing on the single-channel gray level image to obtain a normalized gray level image corresponding to any optical flow image.

And S111c, performing binarization processing on the normalized gray level image to obtain a binary image corresponding to any one of the optical flow images.

In the embodiment of the present disclosure, when the current optical flow image is converted into the binary image, the current optical flow image may be converted into the grayscale image first, and then the binary image corresponding to the optical flow image is obtained through the binarization processing of the grayscale image.

It should be understood that since the optical flow image is a multi-channel image, conversion of the multi-channel optical flow image to a single-channel grayscale image is performed first.

Specifically, the grayscale image may be converted based on the following formula:

wherein, in the formula (2),and representing a single-channel gray image corresponding to the current optical flow image (or the k-th frame optical flow image).

Then, for the single-channel gray image corresponding to the current optical flow imageAnd carrying out normalization processing to ensure that the gray value of the pixel is uniformly distributed between 0 and 255 values to obtain a normalized gray image so as to reduce the interference on the subsequent processing. Further, the conversion processing from the normalized grayscale image to the binary image may be performed.

Here, a critical pixel gray value for performing image binarization may be preset, and a pixel point having a pixel value greater than the critical pixel gray value is determined as a foreground moving object having a pixel value of 255 in the binary image, and correspondingly, a pixel point having a pixel value less than the critical pixel gray value is determined as a background moving object having a pixel value of 0 in the binary image.

For example, image binarization processing may be performed by, for example, the Otsu method OSTU to obtain a binarized image corresponding to the current optical flow image

And S112, in response to the fact that the pixel proportion of the foreground moving object in the binary image is smaller than or equal to a preset proportion threshold value, determining any optical flow image as an optical flow image to be detected with a throwing object.

In the embodiment of the present disclosure, for any optical flow image, the optical flow image f is obtained_kCorresponding binary imageThen, the number of pixels occupied by the foreground moving object in the binary image can be used for representing the probability of the moving object existing in the optical flow image, and then whether the drop object exists in the current optical flow image can be further judged based on the number of pixels occupied by the foreground moving object in the binary image.

Considering the noise caused by excessive moving objects and the negative influence of excessive moving response intensity of the moving objects on high-altitude parabolic detection, a preset ratio threshold of the number of pixels of the moving objects in the binary image can be set, namely a specific threshold which is not exceeded by the ratio of the number of pixels of the moving objects in the binary image to the whole image. At the moment, whether a throwing object exists in the current optical flow image is judged based on the comparison result of the pixel data ratio of the foreground moving object in the binary image and the preset ratio threshold.

Under the condition that the pixel number ratio of the foreground moving object in the binary image is less than or equal to a preset ratio threshold, determining that a falling object exists in the current optical flow image; and otherwise, under the condition that the pixel number ratio of the foreground moving object in the binary image is larger than the preset ratio threshold value, determining that no drop object exists in the current optical flow image.

Based on the operation, the images with the falling objects in the optical flow images can be screened out and used as the optical flow images to be detected for subsequent high-altitude parabolic detection processing.

Therefore, in the embodiment of the disclosure, the single-frame binarization processing may be performed on the current optical flow image, and the optical flow image with the overlarge noise or the abnormal noise is filtered based on the pixel count of the foreground moving object in the binarization image, so as to screen out the optical flow image to be detected with the dropped object, thereby improving the precision and the speed of the high-altitude detection processing.

Based on the foregoing embodiment, in yet another embodiment of the present disclosure, as shown in fig. 6, a schematic flow chart of an implementation of the high altitude parabolic detection method provided in the embodiment of the present disclosure is five, and as shown in fig. 6, the method for determining the position coordinates of the center point of the tossed object in each optical flow image to be detected may include the following steps:

s113, for any optical flow image to be detected, acquiring an initial position coordinate set of a foreground moving object in the binary image, and classifying the foreground moving object in any optical flow image to be detected based on a preset clustering algorithm and the initial position coordinate set to obtain at least one moving object and a coordinate subset corresponding to each moving object.

And S114, calculating the coordinate average value corresponding to the coordinate subset of any moving object for any moving object, and determining the coordinate average value as the position coordinate of the central point of any moving object.

S115, determining a target pixel value at the central point position coordinate of any moving object in the normalized gray-scale image corresponding to any to-be-measured streaming image, determining the moving object with the maximum target pixel value as a falling object, and determining the central point position coordinate corresponding to the moving object with the maximum target pixel value as the central point position coordinate of the falling object.

In the embodiment of the disclosure, for any optical flow image to be detected in which a dropped object exists, a clustering algorithm may be introduced to determine the position coordinates of the center point of the dropped object in the optical flow image to be detected.

In an implementation manner of the embodiment of the present disclosure, in a case that it is determined that a dropped object exists in a current optical flow image, all pixel position coordinates of a foreground moving object in a binary image of the current optical flow image may be acquired to form a two-dimensional array of position coordinates, that is, an initial position coordinate set.

It should be understood that there may be more than one moving object in the current optical flow image, including a true toss, fallen leaves, etc., and that there is not only one true toss. To determine the location of the dropped object, in the embodiments of the present disclosure, the location of the dropped object may be determined by a location clustering algorithm, such as but not limited to a Kmeans clustering algorithm.

The moving objects existing in the current optical flow image can be preliminarily classified by applying a position clustering algorithm. The foreground moving objects can be grouped into at least one type based on the pixel position coordinates of the foreground moving objects as clustering parameters, and if the falling objects are grouped into one type, the leaves are grouped into one type. And determining the cluster corresponding to the true falling object.

In an implementation manner of the embodiment of the present disclosure, for any optical flow image to be detected, an initial position coordinate set of a foreground moving object in a binary image may be obtained, and the initial position coordinate set is clustered based on a preset position clustering algorithm, so as to obtain at least one set of coordinate subsets, where each coordinate subset corresponds to a type of moving object, thereby implementing classification of the moving objects in the optical flow image to be detected based on the position clustering algorithm.

After the classification of the moving objects in the optical flow image to be measured is completed and the coordinate subset of each type of moving objects is obtained, a mean value operation can be performed based on the coordinate subset of each type of moving objects, the determined coordinate mean value is used as the central point position coordinate of each type of moving objects, and the pixel gray value at the corresponding position is determined based on the central point position coordinate from the normalized gray image corresponding to the optical flow image to be measured.

In the embodiment of the disclosure, in order to realize timely alarm processing of a high-altitude parabolic event, the parabolic interval of the high-altitude parabolic event can be rapidly determined by reducing the data volume on the premise of not influencing the detection accuracy of the high-altitude parabolic event. On the premise of not considering the number and the type of the current falling objects, a moving object with the maximum optical flow amplitude in each optical flow image to be detected is used as the falling object, the corresponding central point position coordinate of the moving object is used as the central point position coordinate of the falling object, the interval of the parabolic event is rapidly determined based on the central point position coordinate of the falling object in each optical flow image to be detected, and the high-altitude parabolic event is timely alarmed.

Based on the characteristic that the larger the response intensity/light stream amplitude value is, the larger the pixel value reflected on the gray image is, after the pixel gray value of each moving object at the central point position coordinate in the normalized gray image corresponding to the light stream image to be detected is determined, the moving object of the type with the largest pixel gray value is determined to be the falling object, and the central point position coordinate of the moving object of the type is determined to be the central point position coordinate of the falling object.

It is understood that, in the case where only one fallen object exists currently, the current optical flow image may be a true fallen object in response intensity, i.e., the class with the largest optical flow amplitude, and the weaker response intensity should be a non-fallen object such as a leaf; in the case where there may be a plurality of dropped objects currently, the class in which the response intensity, i.e., the optical-flow amplitude, is the largest in the current optical-flow image may be one of the plurality of dropped objects.

It should be understood that, in the high-altitude parabolic detection process, after performing a high-altitude parabolic alarm, a user may view a parabolic event interval, such as a segment of monitoring video corresponding to a parabolic event, and although the central point position coordinate of a moving object with the maximum optical flow amplitude in an optical flow image to be detected is taken as the position coordinate of the parabolic object under the condition that a plurality of throwing objects may exist, after determining the parabolic event interval, the user may view the parabolic event corresponding to each throwing object that may exist in the interval by viewing the event interval.

Therefore, the central point position coordinate of the moving object with the maximum optical flow amplitude value in each optical flow image to be detected is used as the central point position coordinate of the falling object, the interval of the parabolic event is quickly determined based on the central point position coordinate of the falling object in each optical flow image to be detected, interference objects in the images can be effectively filtered, and the high-altitude parabolic detection speed can be improved on the basis of reducing the data amount of operation processing.

Fig. 7 is a schematic flow chart illustrating a sixth implementation flow of the high altitude parabolic detection method according to the embodiment of the present disclosure, as shown in fig. 7, before determining the motion trajectory of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected, that is, before step 130, the method for performing high altitude parabolic detection processing further includes the following steps:

step 140, determining the initial optical flow image to be measured to the end optical flow image to be measured of the parabolic event corresponding to the falling object from the optical flow images to be measured.

It can be understood that the image acquisition monitoring device continuously acquires images in real time, and there may be a plurality of parabolic events occurring at different times that are acquired by the image acquisition monitoring device; that is to say, the optical flow images to be measured belonging to the same parabolic event are generally continuous in frame, or in the case where there are few objects sheltered from the objects during the falling process of the falling objects, the frame interval between the optical flow images to be measured belonging to the same parabolic event is less than a certain threshold. Accordingly, if the frame interval between the optical flow images to be measured is too far away, the two frames of images may correspond to parabolic events occurring at different times.

In the embodiment of the disclosure, different parabolic events occurring at different times can be divided based on the frame interval value of the optical flow image to be measured, and the start frame and the end frame corresponding to the parabolic events occurring at different times are determined.

In an embodiment, a frame interval threshold between different parabolic events may be preset, and a determination of an interval value between a previous frame of optical flow image frame to be measured and an optical flow image to be measured in which a falling object exists, which is obtained after a single frame of processing, is sequentially performed to determine whether the previous two frames of optical flow images to be measured belong to the same parabolic event, so as to update a start frame and an end frame of the parabolic event.

For example, the first image and the second image of the two frames of optical flow images to be measured are taken as an example for description.

The frame interval value of the current second image and the first image may be calculated after determining that the first image belongs to the first parabolic event and determining the first image as the end frame corresponding to the first parabolic event. Under the condition that the frame interval value is smaller than or equal to a preset frame interval threshold value, determining that the second image and the first image belong to the same parabolic event, and updating an end frame of the first parabolic event into the second image; correspondingly, in the case that the frame interval value is greater than the preset frame interval threshold value, it is determined that the second image and the first image do not belong to the same parabolic event, which indicates that the ending frame of the first parabolic event is the first image, and the second image belongs to a new parabolic event, i.e., a second parabolic event, at which time the second image may be determined as the starting frame of the new second parabolic event.

Correspondingly, after the parabolic event to which the second image belongs is determined, the parabolic event to which the next frame of optical flow image to be measured, that is, the third image belongs may be continuously determined, if the frame interval between the third image and the second image is less than the preset frame interval threshold, the third image and the second image belong to the same second parabolic event, and at this time, the end frame of the second parabolic event may be determined as the third image. Repeating the step, continuing to determine the frame interval value between the next frame of optical flow image to be detected and the third image, if the frame interval value is still smaller than the preset frame interval threshold value, updating the ending frame of the second parabolic event to be the next frame of optical flow image to be detected, and repeating the step until the frame interval value between the optical flow image to be detected and the previous frame of optical flow image to be detected is larger than the preset frame interval threshold value, determining the previous frame of optical flow image to be detected to be the final ending frame of the second parabolic event, so that the starting optical flow image to be detected and the ending optical flow image to be detected of a complete parabolic event can be determined, and determining the occurrence interval of the parabolic event.

For example, the logic code for determining the parabolic event to which the optical flow image under test belongs initializes the parabolic event start frame index d as follows_s0, end frame index d_eWhen it is 0, let δ_eFor the frame interval threshold between parabolic events, the starts is initialized at the same time]，ends＝[]For recording the start and end frames of each parabolic event.

For i＝0：frame_num Do

If this frame has falling object

If the current optical flow image has a throwing object, determining the optical flow image to be measured

If d_c-d_e＞δ_e

δ_eFor a preset frame interval threshold, the current frame index is d_cDifference greater than frame interval threshold

d_s＝d_c

starts.append(d_s)

Else:

d_e＝d_c

End if

Else if d_c-d_e＞δ_e

The difference being less than the frame interval threshold

If len(ends)＝＝0 or ends[-1]！＝d_e:

ends.append(d_e)

End if

End if

ends.pop(0)

Based on the logic code, the interval of each parabolic event, i.e. the start frame to end frame list starts, ends, can be returned.

Here, the determination process of the motion event to be measured is cycled based on the time sequence of the optical flow image to be measured, so that the starting frame and the ending frame corresponding to different parabolic events, namely the starting optical flow image to be measured to the ending optical flow image of the parabolic event, can be determined.

In some embodiments, after the section where the parabolic event corresponding to the dropped object occurs is determined, the position coordinates of the central point of the parabolic object in each frame of optical flow images to be measured from the start optical flow image to the end optical flow image of the parabolic event may be interpolated according to the time sequence, and the position coordinates of the central point in each optical flow image to be measured of the dropped object in the section of the parabolic event are mapped on one plane image and connected to form the motion trajectory of the parabolic object.

Therefore, in the embodiment of the disclosure, the start frame and the end frame of different motion events to be detected can be determined based on the frame interval difference, so as to accurately divide the different motion events to be detected, and improve the precision of high-altitude parabolic detection processing.

Based on the foregoing embodiment, in yet another implementation manner of the embodiment of the present disclosure, fig. 8 is a seventh implementation flow diagram of the high altitude parabolic detection method provided in the embodiment of the present disclosure, and as shown in fig. 8, the method for the high altitude parabolic detection device to execute the high altitude parabolic detection processing based on the motion trajectory further includes the following steps:

s121, performing linear fitting processing on the motion track of the falling object to obtain a fitted straight line corresponding to the motion track, and determining an included angle between the fitted straight line and the vertical direction.

And S122, determining a central point position pixel value from the corresponding normalized gray level image based on the central point position coordinates of the falling object in each optical flow image to be detected in the parabolic event, and accumulating the central point position pixel values to obtain a pixel accumulated value.

And S123, performing difference value operation of the vertical coordinates on the central point position coordinate of the falling object in the initial optical flow image to be detected and the central point position coordinate of the falling object in the ending optical flow image to be detected in the parabolic event to obtain a coordinate difference value.

And S124, executing high-altitude parabolic detection processing based on the included angle between the fitted straight line and the vertical direction, the pixel accumulated value and the coordinate difference value.

In the embodiment of the present disclosure, interpolation processing is performed on coordinates of a central point position of a dropped object in each optical flow image according to a time sequence from a start of the optical flow image to be detected to an end of the optical flow image to be detected of a parabolic event, so that after a motion trajectory of the dropped object is obtained, high-altitude parabolic detection of the parabolic event can be realized based on the motion trajectory.

It should be understood that the motion trajectory of the falling object does not satisfy a certain function rule, but has a certain regularity, and in the embodiment of the present disclosure, a straight line fitting method may be introduced to perform straight line fitting processing on the motion trajectory of the falling object, so as to obtain a fitted straight line corresponding to the motion trajectory.

In one embodiment, since the parabolic event is not a true high-altitude parabolic event, and may be similar to a human being who has delivered an object by hand, or has lifted the object over the head, for example, the event person stands at a window to eat an apple, and holds the apple with his hand and puts it down by biting his mouth, an angle between the fitted straight line and the vertical direction can be calculated, and the angle can be used as one of the determination factors for determining whether the parabolic event is a high-altitude parabolic event.

For example, fig. 9 is a schematic diagram of an included angle between a trajectory fitting straight line and a vertical direction provided in the embodiment of the present disclosure, and as shown in fig. 9, an included angle between the fitted straight line and the vertical direction is θ.

In another embodiment, the pixel values at the position coordinates of the center point of the falling object are determined from the normalized grayscale images corresponding to the optical flow images to be measured from the start optical flow image to be measured to the end optical flow image to be measured of the parabolic event, and the pixel values are subjected to accumulation and summation processing to obtain a pixel accumulated value, and the pixel accumulated value is used as one of the judgment factors for judging whether the parabolic event is a high-altitude parabolic event.

In another embodiment, it is understood that a true high altitude parabolic event is intended to have a range of altitude parabolas. For example, a parabolic event with a height above three floors belongs to a high-altitude parabolic event, and therefore, the vertical coordinate of the falling object in the initial optical flow image to be measured of the parabolic event, that is, the maximum vertical coordinate, may be determined based on the above embodiment, that is, the highest point of the parabolic event; and performing difference operation with the ordinate of the ending frame of the parabolic event, namely the minimum ordinate, namely the lowest point of the parabolic event, and then determining the height difference value between the lowest point and the highest point of the parabolic event, namely the parabolic height, based on the coordinate difference value between the maximum ordinate and the minimum ordinate, wherein the height difference value can also be used as one of judgment factors for judging whether the parabolic event is a high-altitude parabolic event.

In the embodiment of the disclosure, whether the parabolic event belongs to a high altitude parabolic event or not can be further judged by combining the included angle between the fitted straight line and the vertical direction, the accumulated value of the pixel values of the central point position of the falling object and the height difference value between the lowest point and the highest point of the parabolic event.

Fig. 10 is a schematic diagram illustrating an implementation flow of a high-altitude parabolic detection method according to an embodiment of the present disclosure, as shown in fig. 10, a method for performing high-altitude parabolic detection processing on a parabolic event by using high-altitude parabolic detection equipment based on an included angle between a fitted straight line and a vertical direction, a pixel accumulated value, and a coordinate difference value may include the following steps:

s124, in response to the fact that the included angle between the fitted straight line and the vertical direction is smaller than a preset angle threshold value, the pixel accumulated value is smaller than a preset pixel threshold value, and the coordinate difference value is larger than a preset height threshold value, it is determined that the falling object is a high-altitude falling object, and the corresponding parabolic event is a high-altitude parabolic event.

S124, 124b, determining that the falling object is not a high-altitude falling object and the corresponding parabolic event is not a high-altitude parabolic event in response to the fact that the included angle between the fitted straight line and the vertical direction is greater than or equal to a preset angle threshold value, or the pixel accumulated value is greater than or equal to a preset pixel threshold value and the coordinate difference value is less than or equal to a preset height threshold value.

In an implementation manner of the embodiment of the disclosure, an included angle threshold, a pixel threshold, and a height threshold of a high-altitude parabolic event are preset, an included angle between a straight line after the parabolic event is fitted and a vertical direction is compared with the preset included angle threshold, an accumulated value of pixel values at a central point position of a falling object is compared with the preset pixel threshold, a height difference value between a lowest point and a highest point of the parabolic event is compared with the preset height threshold, and meanwhile, a comparison result of three determination factors is combined to determine whether the falling object is a high-altitude falling object, in other words, whether the parabolic event corresponding to the falling object is a high-altitude parabolic event is determined.

And determining that the falling object is a high-altitude falling object and the corresponding parabolic event is a high-altitude parabolic event under the conditions that the included angle between the straight line and the vertical direction after the parabolic event is fitted is smaller than a preset included angle threshold value, the accumulated value of the pixel values of the central point position of the falling object is smaller than a preset pixel threshold value and the height difference value between the lowest point and the highest point of the parabolic event is larger than a preset height threshold value, namely the three judgment factors simultaneously meet the corresponding preset threshold value conditions.

In another embodiment of the present disclosure, in a case that at least one of the three determination factors does not satisfy the corresponding preset threshold condition, it is determined that the falling object is not a true high-altitude parabolic object, and the corresponding parabolic event is not a high-altitude parabolic event.

Determining that the falling object does not belong to a high-altitude falling object under the condition that the included angle between the straight line and the vertical direction after the motion event to be detected is fitted is greater than or equal to a preset included angle threshold value; or determining that the falling object does not belong to a high-altitude falling object under the condition that the accumulated value of the pixel values of the central point position of the falling object is greater than or equal to a preset pixel threshold value; or determining that the falling object does not belong to a high-altitude falling object under the condition that the height difference value between the lowest point and the highest point of the parabolic event is smaller than or equal to a preset height threshold value. Or determining that the falling object does not belong to a high-altitude falling object under the condition that the included angle between the straight line and the vertical direction after the motion event to be detected is fitted is greater than or equal to a preset included angle threshold value and the accumulated value of the pixel values of the central point position of the falling object is greater than or equal to a preset pixel threshold value; or determining that the falling object does not belong to a high-altitude falling object under the condition that the included angle between the straight line and the vertical direction after the fitting of the motion event to be detected is greater than or equal to a preset included angle threshold value and the height difference value between the lowest point and the highest point of the parabolic event is less than or equal to a preset height threshold value; or under the condition that the accumulated value of the pixel values of the central point position of the falling object is greater than or equal to a preset pixel threshold value and the height difference value between the lowest point and the highest point of the parabolic event is less than or equal to a preset height threshold value, determining that the falling object does not belong to a high-altitude falling object; or determining that the falling object does not belong to a high-altitude falling object and the corresponding parabolic event is not a high-altitude parabolic event under the conditions that the included angle between the straight line and the vertical direction after the motion event to be detected is fitted is greater than or equal to a preset included angle threshold value, the accumulated value of the pixel values of the central point position of the falling object is greater than or equal to a preset pixel threshold value, and the height difference value between the lowest point and the highest point of the parabolic event is less than or equal to a preset height threshold value.

Therefore, in the embodiment of the disclosure, after the complete track reduction is performed by combining the detection information of the falling object on multiple frames based on the event detection method, the high-altitude parabolic event can be accurately judged based on the motion track.

Based on the above embodiment, in yet another implementation manner of the embodiment of the present disclosure, the method for performing high-altitude parabolic detection processing mainly includes four major parts, namely data preprocessing, dense optical flow calculation, single-frame post-processing, and multi-frame post-processing.

The data preprocessing specifically comprises the following steps:

s201, acquiring an initial image, and determining a target detection area from the initial image based on a preset polygon outline.

S202, generating a minimum detection frame based on the target detection area, and performing image segmentation processing on the initial image based on the minimum detection frame to obtain an image to be detected.

The dense optical flow calculation specifically comprises the following steps:

s203, reading two adjacent images to be detected in sequence in each period of the execution cycle process, and generating optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model.

The single-frame post-processing specifically comprises the following steps:

and S204, carrying out single-channel gray scale conversion processing on the optical flow image to obtain a single-channel gray scale image corresponding to the optical flow image.

S205, normalization processing is carried out on the single-channel gray image, and a normalized gray image corresponding to the optical flow image is obtained.

S206, carrying out binarization processing on the normalized gray level image to obtain a binary image corresponding to the optical flow image; the binary image comprises a foreground moving object with a first pixel value and a background non-moving object with a second pixel value.

S207, determine whether the pixel proportion of the foreground moving object in the binary image is less than or equal to a preset proportion threshold? If so, go to S208; if not, jumping to execute S204, and continuing to process the optical flow image of the next frame.

Under the condition that the pixel proportion of the foreground moving object in the binary image is smaller than or equal to a preset proportion threshold value, determining that the current optical flow image is the optical flow image to be detected with the falling object; otherwise, it is absent.

And S208, acquiring an initial position coordinate set of the foreground moving object in the binary image.

S209, classifying the foreground moving objects based on a preset clustering algorithm and the initial position coordinate set to obtain a coordinate subset corresponding to each moving object.

S210, calculating a coordinate average value corresponding to the coordinate subset of any moving object for any moving object, and determining the coordinate average value as the position coordinate of the central point of any moving object.

S211, determining a target pixel value at the position coordinate of the central point of any moving object in the normalized gray-scale image corresponding to any to-be-measured streaming image, determining the moving object with the maximum target pixel value as a falling object, and determining the position coordinate of the central point corresponding to the moving object with the maximum target pixel value as the position coordinate of the central point of the falling object.

The multi-frame post-processing comprises the following steps:

s213, calculating the frame interval value between the current optical flow image to be measured and the historical previous optical flow image to be measured.

S214, determine whether the frame interval value is less than or equal to the preset interval threshold? If so, go to S215; if not, the jump is performed S213.

If the frame interval value between the current optical flow image value to be detected and the historical previous optical flow image to be detected is smaller than or equal to a preset interval threshold value, the current optical flow image to be detected and the historical previous optical flow image to be detected belong to the same parabolic event, and an end frame of the parabolic event is updated based on the current optical flow image to be detected; otherwise, the optical flow images to be measured do not belong to the same parabolic event, and the current optical flow image to be measured is the initial frame of the new parabolic event.

S215, determine whether to determine the start frame to the end frame of the parabolic event? If so, go to S216; if not, jumping to execute S213, and continuing the loop process from S213 to S214 until the beginning frame to the ending frame of the parabolic event are taken.

S216, according to the sequence of the parabolic event starting frame to the parabolic event ending frame from first to last, interpolation processing is carried out on the coordinates of the central point position of the falling object in each optical flow image to be detected, and the motion trail of the falling object is obtained.

S217, performing linear fitting on the motion track of the falling object, and calculating an included angle theta between the fitted linear line and the vertical direction; summing pixel gray values of the central point position of the falling object on each frame of the parabolic event to obtain a pixel accumulated value V; calculating the height difference value of the lowest point and the highest point of the parabolic event, namely the parabolic height H;

s218, judging whether theta is smaller than a threshold value T₁Whether V is less than threshold T₂And whether H is greater than a threshold T₃(ii) a If yes, go to step S219, otherwise, go to step S220.

Wherein, T₁For presetting a threshold value of the included angle, T₂For presetting the pixel threshold, T₃A height threshold is preset.

And S219, the throwing object is a high-altitude throwing object, and the throwing event is a high-altitude throwing event.

And determining that the falling object is a high-altitude falling object and the parabolic event is a high-altitude parabolic event under the condition that the included angle between the straight line and the vertical direction after the parabolic event is fitted is smaller than a preset included angle threshold value, the accumulated value of the pixel values of the central point position of the falling object is smaller than a preset pixel threshold value and the height difference value between the lowest point and the highest point of the parabolic event is larger than a preset height threshold value, namely the three judgment factors simultaneously meet the corresponding preset threshold value conditions.

And S220, the falling object is not a high-altitude falling object, and the parabolic event is not a high-altitude parabolic event.

After the parabolic event is fitted, the included angle between the straight line and the vertical direction is greater than or equal to a preset included angle threshold value, or the accumulated value of the pixel values of the central point position of the falling object is greater than or equal to a preset pixel threshold value, or the height difference value between the lowest point and the highest point of the parabolic event is less than or equal to a preset height threshold value, namely that the falling object is determined not to be a high-altitude falling object and the parabolic event is determined not to be a high-altitude parabolic event under the condition that at least one judgment factor in the three judgment factors does not meet the corresponding preset threshold value condition.

Fig. 11 is a scene schematic diagram of a high-altitude parabolic detection method according to an embodiment of the disclosure, and as shown in fig. 11, in a process of performing high-altitude parabolic detection on a building, image acquisition is performed by a camera to obtain an initial image, and then preprocessing is performed, including determining an interested region based on a preset polygon profile and performing image clipping based on a minimum frame, to obtain an nth frame of image to be detected and an (n +1) th frame of image to be detected; then inputting two adjacent frames of images to be detected into a preset optical flow model constructed by a convolutional neural network and a cyclic neural network to obtain a dense optical flow graph; continuously carrying out single-frame post-processing on each frame of optical flow image, wherein the single-frame post-processing comprises judging whether a thrown object exists in the image or not based on image binarization, and determining the position coordinate of the center point of the thrown object based on position clustering on the optical flow image to be detected with the thrown object; and circulating the processes to obtain multiple frames of optical flow images to be detected, performing multiple frames of post-processing on the multiple frames of optical flow images to be detected until a starting frame to an ending frame of the parabolic event corresponding to the falling object is obtained, restoring the motion track of the falling object, detecting whether the falling object is a high-altitude falling object or not based on the motion track, and giving a time alarm to the high-altitude parabolic event when the falling object is the high-altitude falling object, namely the corresponding parabolic time is the high-altitude parabolic event.

Therefore, the high-altitude parabolic detection method provided by the embodiment of the disclosure adopts the optical flow model constructed based on deep learning to generate the dense optical flow graph to realize detection of the moving object, and has the advantages of good robustness, high precision, shorter time consumption and less noise; on the other hand, on the basis of detecting moving objects by using a preset optical flow model, a robust post-processing method of adding a plurality of frames to a single frame is also provided, the single frame with the interferent is filtered based on image binarization processing and a position clustering algorithm, the accurate position of the falling object is obtained, then the trajectory of the high-altitude parabolic event is restored and detected by combining the detection information of the falling object on the plurality of frames, and the efficiency and the precision of the high-altitude parabolic detection are further improved.

Based on the above embodiments, in an embodiment of the present disclosure, fig. 12 is a schematic structural diagram of a high altitude parabolic detection apparatus proposed in an embodiment of the present disclosure, and as shown in fig. 12, the high altitude parabolic detection apparatus 10 includes an obtaining unit 11, a generating unit 12, a determining unit 13, and a processing unit 14.

The acquiring unit 11 is configured to sequentially read two adjacent images to be detected from a plurality of frames of images to be detected according to the time sequence of image acquisition;

the generating unit 12 is configured to generate optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model;

a determination unit 13 configured to determine optical flow images to be measured in which a dropped object exists from the optical flow images, and determine center point position coordinates of the dropped object in each optical flow image to be measured;

the determining unit 13 is further configured to determine a motion trajectory of the falling object according to the position coordinates of the center point of the falling object in each optical flow image to be detected;

a processing unit 14 configured to perform a high altitude parabolic detection process based on the motion trajectory.

In some embodiments, the determining unit 13 is configured to generate, for any optical flow image, a binary image corresponding to the any optical flow image; the binary image comprises a foreground moving object with a first pixel value and a background non-moving object with a second pixel value; and determining any optical flow image as the optical flow image to be detected with the falling object in response to the pixel proportion of the foreground moving object in the binary image being less than or equal to a preset proportion threshold value.

In some embodiments, the determining unit 13 is configured to perform single-channel grayscale conversion processing on any optical flow image to obtain a single-channel grayscale image corresponding to any optical flow image; carrying out normalization processing on the single-channel gray level image to obtain a normalized gray level image corresponding to any optical flow image; and carrying out binarization processing on the normalized gray level image to obtain the binary image corresponding to any optical flow image.

In some embodiments, the determining unit 13 is configured to, for any optical flow image to be detected, obtain an initial position coordinate set of the foreground moving object in the binary image, and perform classification processing on the foreground moving object in the optical flow image to be detected based on a preset clustering algorithm and the initial position coordinate set, so as to obtain at least one moving object and a coordinate subset corresponding to each moving object; for any moving object, calculating a coordinate average value corresponding to the coordinate subset of the moving object, and determining the coordinate average value as the central point position coordinate of the moving object; and determining a target pixel value at the position coordinate of the central point of any moving object in the normalized gray-scale image corresponding to any to-be-detected flow image, determining the moving object with the maximum target pixel value as the falling object, and determining the position coordinate of the central point corresponding to the moving object with the maximum target pixel value as the position coordinate of the central point of the falling object.

In some embodiments, before determining the motion trajectory of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be measured, the determining unit 13 is configured to determine, from each optical flow image to be measured, a start optical flow image to be measured to an end optical flow image to be measured of a parabolic event corresponding to the falling object.

In some embodiments, the determining unit 13 is configured to perform interpolation processing on coordinates of a central point position of the falling object in each optical flow image to be detected in the parabolic event according to a time sequence, so as to obtain a motion trajectory of the falling object.

In some embodiments, the optical flow image under test comprises at least a first image and a second image, the first image being a current ending optical flow image under test of a first parabolic event; the first image and the second image are two continuous frames of optical flow images to be detected, and the determining unit 13 is configured to calculate a frame interval value between the second image and the first image; and in response to the frame interval value being less than or equal to a preset interval threshold, updating the second image to the ending optical flow to be measured image of the first parabolic event; and determining the second image as a starting optical flow image to be tested of a second parabolic event in response to the frame interval value being greater than the preset interval threshold.

In some embodiments, the processing unit 14 is configured to perform a straight line fitting process on the motion trajectory of the falling object, obtain a fitted straight line corresponding to the motion trajectory, and determine an included angle between the fitted straight line and the vertical direction; determining a central point position pixel value from the corresponding normalized gray level image based on the central point position coordinates of the falling object in each optical flow image to be detected in the parabolic event, and performing accumulation processing on the central point position pixel values to obtain a pixel accumulated value; and in the parabolic event, performing difference value operation of vertical coordinates on the position coordinate of the central point of the falling object in the initial optical flow image to be detected and the position coordinate of the central point of the falling object in the ending optical flow image to be detected to obtain a coordinate difference value; and executing the high altitude parabolic detection processing based on the included angle between the fitted straight line and the vertical direction, the pixel accumulated value and the coordinate difference value.

In some embodiments, the processing unit 14 is configured to determine that the falling object is a high-altitude falling object and the corresponding parabolic event is a high-altitude parabolic event in response to that an included angle between the fitted straight line and the vertical direction is smaller than a preset angle threshold, the accumulated pixel value is smaller than the preset pixel threshold, and the coordinate difference value is larger than a preset height threshold.

In some embodiments, the obtaining unit 11 is further configured to obtain an initial image, and determine a target detection area from the initial image based on a preset polygon profile; and generating a minimum detection frame based on the target detection area, and performing image segmentation processing on the initial image based on the minimum detection frame to obtain the image to be detected.

In an embodiment of the present disclosure, further, fig. 13 is a schematic diagram illustrating a composition structure of a high altitude parabolic detection apparatus provided in an embodiment of the present disclosure, as shown in fig. 13, a high altitude parabolic detection apparatus 20 provided in an embodiment of the present disclosure may further include a processor 21, a memory 22 storing executable instructions of the processor 21, and further, the living body detection apparatus 20 may further include a communication interface 23, and a bus 24 for connecting the processor 21, the memory 22, and the communication interface 23.

In the embodiment of the present disclosure, the Processor 21 may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a ProgRAMmable Logic Device (PLD), a Field ProgRAMmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that the electronic devices for implementing the above-described processor functions may be other devices, and the embodiments of the present disclosure are not particularly limited. The biopsy device 20 may further comprise a memory 22, which memory 22 may be connected to the processor 21, wherein the memory 22 is adapted to store executable program code comprising computer operating instructions, and wherein the memory 22 may comprise a high speed RAM memory and may further comprise a non-volatile memory, e.g. at least two disk memories.

In the embodiment of the present disclosure, the bus 24 is used to connect the communication interface 23, the processor 21, and the memory 22 and the intercommunication among these devices.

In an embodiment of the present disclosure, memory 22 is used to store instructions and data.

Further, in the embodiment of the present disclosure, the processor 21 is configured to sequentially read two adjacent images to be measured from the multiple frames of images to be measured according to a time sequence of image acquisition, and generate an optical flow image corresponding to the two adjacent images to be measured through a preset optical flow model; determining optical flow images to be detected with falling objects from the optical flow images, and determining the position coordinates of the central points of the falling objects in the optical flow images to be detected; determining the motion trail of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected; and executing high-altitude parabolic detection processing based on the motion trail.

In practical applications, the Memory 22 may be a volatile Memory (volatile Memory), such as a Random-Access Memory (RAM); or a non-volatile Memory (non-volatile Memory), such as a Read-Only Memory (ROM), a flash Memory (flash Memory), a Hard Disk (Hard Disk Drive, HDD) or a Solid-State Drive (SSD); or a combination of the above types of memories and provides instructions and data to the processor 21.

In addition, each functional module in this embodiment may be integrated into one recommendation unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware or a form of a software functional module.

Based on the understanding that the technical solution of the present embodiment essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, and include several instructions for causing a high altitude parabolic detection device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method of the present embodiment. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The embodiment of the disclosure provides high-altitude parabolic detection equipment, which sequentially reads two adjacent images to be detected from a plurality of frames of images to be detected according to the time sequence of image acquisition and generates optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model; determining to-be-detected optical flow images with the falling objects in the optical flow images, and determining the position coordinates of the central points of the falling objects in the to-be-detected optical flow images; determining the motion trail of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected; and executing high-altitude parabolic detection processing based on the motion trail. Therefore, on one hand, the dense light flow graph is generated by adopting the light flow model constructed based on the deep learning so as to realize the detection of the moving object, the robustness is good, the precision is high, the time consumption is shorter, and the noise is less; on the other hand, on the basis of detecting moving objects by using a preset optical flow model, the method also provides a robust single-frame and multi-frame post-processing method, the single frame with the interferent is filtered, the single-frame image with the falling object is found out, after the single-frame false detection is removed, the track restoration and the high-altitude parabolic event detection are carried out by combining the position information of the falling object on the multi-frame, and the efficiency and the precision of the high-altitude parabolic detection are further improved.

Embodiments of the present disclosure provide a computer-readable storage medium on which a program is stored, which when executed by a processor implements a high altitude parabolic detection method as described above.

Specifically, the program instructions corresponding to a high altitude parabola detection method in the present embodiment may be stored on a storage medium such as an optical disc, a hard disc, a U-disc, etc., and when the program instructions corresponding to a high altitude parabola detection method in the storage medium are read or executed by an electronic device, the method includes the following steps:

sequentially reading two adjacent images to be detected from a plurality of frames of images to be detected according to the time sequence of image acquisition, and generating optical flow images corresponding to the two adjacent images to be detected through a preset optical flow model;

determining optical flow images to be detected with falling objects from the optical flow images, and determining the position coordinates of the central points of the falling objects in the optical flow images to be detected;

determining the motion trail of the falling object according to the position coordinates of the central point of the falling object in each optical flow image to be detected;

and executing high-altitude parabolic detection processing based on the motion trail.

Accordingly, the embodiments of the present disclosure further provide a computer program product, where the computer program product includes computer-executable instructions for implementing the steps in the high altitude parabolic detection method proposed by the embodiments of the present disclosure.

As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of implementations of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable high altitude parabolic detection apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable high altitude parabolic detection apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable high altitude parabolic sensing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable high altitude parabolic sensing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks in the flowchart and/or block diagram block or blocks.

The above description is only for the preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure.