1. An identity recognition method, characterized in that the identity recognition method comprises:

acquiring a video image of a target vehicle in a specific area;

preprocessing the video image to obtain a vehicle photo and a face photo of the target vehicle, wherein the vehicle photo comprises a vehicle number plate photo and a vehicle appearance photo of a plurality of shooting angles;

calculating the shooting angle of the vehicle appearance picture, and inquiring a mapping relation between a preset shooting angle and a picture detection model according to the shooting angle to obtain a target picture detection model;

calling the target photo detection model to process the vehicle number plate photo and the vehicle appearance photo corresponding to the shooting angle to obtain vehicle characteristic data, wherein the vehicle characteristic data comprises vehicle number plate data and vehicle appearance data;

calling a face recognition model to process the face photo to obtain personnel feature data;

detecting whether the vehicle characteristic data is matched with the personnel characteristic data;

and outputting an alarm prompt when the detection result is that the vehicle characteristic data is not matched with the personnel characteristic data.

2. The method of claim 1, wherein the preprocessing the video image to obtain a picture of a vehicle and a picture of a human face of the target vehicle comprises:

splitting the video image according to a preset frame rate to obtain a track image set consisting of a plurality of frame track images;

detecting whether a face image and a license plate image exist in the track image set;

when the detection result is that the face images and the license plate images exist in the track image set, selecting a target face image and a target license plate image of which the image definition is greater than a preset definition threshold;

selecting target vehicle images of a plurality of shooting angles from the track image set;

and combining the target face image, the target license plate image and the target vehicle image to obtain a target image set.

3. The method of claim 2, wherein the selecting the target vehicle images from the set of track images from a plurality of camera angles comprises:

determining a model training angle corresponding to a pre-trained photo detection model, wherein the model training angle has a mapping relation with the shooting angle;

positioning the target vehicle according to a target angle serving as a reference angle, and calculating an angle difference value between the model training angle and the target angle;

and selecting the vehicle image corresponding to the angle difference as a target vehicle image of a plurality of shooting angles corresponding to the model training angles.

4. The identity recognition method of claim 1, wherein the invoking of the face recognition model to process the face photograph to obtain the person feature data comprises:

positioning a face area in the face photo;

extracting target feature data of the face region by using features;

and storing the target characteristic data according to a preset data format to obtain the personnel characteristic data.

5. The identification method of claim 1, wherein the detecting whether the vehicle characteristic data and the person characteristic data match comprises:

acquiring the vehicle characteristic data;

traversing a preset mapping relation between the vehicle characteristics and the personnel characteristics according to the vehicle characteristic data to obtain target personnel characteristic data;

detecting whether the target person characteristic data is consistent with the person characteristic data;

when the detection result is that the target person characteristic data is consistent with the person characteristic data, determining that the vehicle characteristic data is matched with the person characteristic data;

and when the detection result is that the target person characteristic data is inconsistent with the person characteristic data, determining that the vehicle characteristic data is not matched with the person characteristic data.

6. The method of claim 1, further comprising:

acquiring the vehicle license plate data;

traversing a preset mapping relation between the vehicle license plate and the vehicle appearance according to the vehicle license plate data to obtain target vehicle appearance data;

detecting whether the target vehicle appearance data is consistent with the vehicle appearance data;

when the detection result is that the target vehicle appearance data is consistent with the vehicle appearance data, determining that the vehicle license plate data is matched with the vehicle appearance data;

and when the detection result is that the target vehicle appearance data is inconsistent with the vehicle appearance data, determining that the vehicle number plate data is not matched with the vehicle appearance data.

7. The identification method of claim 1, wherein prior to said capturing the video image of the target vehicle within the particular area, the method further comprises:

acquiring basic information of a pre-purchased vehicle, automatically analyzing the basic information based on a pre-trained vehicle standard judgment model to judge that the pre-purchased vehicle meets the national vehicle standard, and generating a target two-dimensional code aiming at the basic information;

determining personnel information of the pre-purchased vehicle, and detecting whether the personnel information is correct based on a face recognition technology;

when the detection result is that the personnel information is correct, determining the basic information of the pre-purchased vehicle through the target two-dimensional code, and binding the personnel information with the basic information to generate a vehicle number plate;

after the pre-purchased vehicle is provided with the vehicle number plate, vehicle photos of a plurality of shooting angles corresponding to the pre-purchased vehicle are obtained, and the vehicle photos are input into a photo detection model corresponding to the shooting angles so as to judge that the vehicle photos meet the photo detection requirements.

8. An identification device, comprising:

the video acquisition module is used for acquiring a video image of a target vehicle in a specific area;

the video processing module is used for preprocessing the video image to obtain a vehicle photo and a face photo of the target vehicle, wherein the vehicle photo comprises a vehicle number plate photo and a vehicle appearance photo of a plurality of shooting angles;

the angle calculation module is used for calculating the shooting angle of the vehicle appearance photo and inquiring the mapping relation between the preset shooting angle and the photo detection model according to the shooting angle to obtain a target photo detection model;

the vehicle data acquisition module is used for calling the target photo detection model to process the vehicle number plate photo and the vehicle appearance photo corresponding to the shooting angle to obtain vehicle characteristic data, wherein the vehicle characteristic data comprises vehicle number plate data and vehicle appearance data;

the personnel data acquisition module is used for calling a face recognition model to process the face photo to obtain personnel characteristic data;

the data detection module is used for detecting whether the vehicle characteristic data is matched with the personnel characteristic data;

and the warning prompt module is used for outputting warning prompts when the detection result is that the vehicle characteristic data is not matched with the personnel characteristic data.

9. A computer device, characterized in that the computer device comprises a processor for implementing the identity recognition method according to any one of claims 1 to 7 when executing a computer program stored in a memory.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the identification method according to any one of claims 1 to 7.

Background

With the acceleration of the urbanization process and the improvement of the living standard of residents in China and the deep mind of the green and environment-friendly trip concept, the electric bicycle becomes an important tool for short-distance trips of the majority of citizens, and the number of the electric bicycles is also highly increased. By 2019, according to preliminary statistics, the social conservation quantity of the electric bicycles at present exceeds 2.5 hundred million. At present, the electric bicycle does not need strict driving examination and other procedures when running on the road like a motor vehicle, and the traffic safety problem is easily caused. At present, the electric bicycles are propelled to be on the market at all parts of the country, and the management and control work of the electric bicycles is strengthened, so that illegal behaviors of the electric bicycles are urgently needed to be detected and identified through the existing bayonet cameras or newly-added cameras.

In the process of implementing the present application, the inventor finds that the following technical problems exist in the prior art: in the prior art, when the identification of the electric bicycle is realized, only the license plate number information of the electric bicycle is collected, and then the corresponding illegal person information is matched from the database, and in the using process of the real electric bicycle, many conditions can not ensure that the electric bicycle conforms to the regulation of one person for one license plate, so that the problem of searching for errors by the illegal person is caused.

Therefore, it is necessary to provide an identification method capable of improving the accuracy of identification of the electric bicycle.

Disclosure of Invention

In view of the above, there is a need for an identity recognition method, an identity recognition apparatus, a computer device and a medium, which can improve the accuracy of identity recognition.

A first aspect of an embodiment of the present application provides an identity recognition method, where the identity recognition method includes:

acquiring a video image of a target vehicle in a specific area;

preprocessing the video image to obtain a vehicle photo and a face photo of the target vehicle, wherein the vehicle photo comprises a vehicle number plate photo and a vehicle appearance photo of a plurality of shooting angles;

calculating the shooting angle of the vehicle appearance picture, and inquiring a mapping relation between a preset shooting angle and a picture detection model according to the shooting angle to obtain a target picture detection model;

calling the target photo detection model to process the vehicle number plate photo and the vehicle appearance photo corresponding to the shooting angle to obtain vehicle characteristic data, wherein the vehicle characteristic data comprises vehicle number plate data and vehicle appearance data;

calling a face recognition model to process the face photo to obtain personnel feature data;

detecting whether the vehicle characteristic data is matched with the personnel characteristic data;

and outputting an alarm prompt when the detection result is that the vehicle characteristic data is not matched with the personnel characteristic data.

Further, in the above identity recognition method provided in the embodiment of the present application, the preprocessing the video image to obtain a vehicle photograph and a face photograph of the target vehicle includes:

splitting the video image according to a preset frame rate to obtain a track image set consisting of a plurality of frame track images;

detecting whether a face image and a license plate image exist in the track image set;

when the detection result is that the face images and the license plate images exist in the track image set, selecting a target face image and a target license plate image of which the image definition is greater than a preset definition threshold;

selecting target vehicle images of a plurality of shooting angles from the track image set;

and combining the target face image, the target license plate image and the target vehicle image to obtain a target image set.

Further, in the above identity recognition method provided in the embodiment of the present application, the invoking the photo detection model to process the vehicle photo to obtain the vehicle feature data includes:

acquiring a license plate region in the vehicle license plate photo, and performing feature extraction on feature data of the license plate region to obtain vehicle license plate data;

acquiring the vehicle appearance photo, and calculating the shooting angle of the vehicle appearance photo;

and determining a target photo detection model corresponding to the shooting angle, and calling the target detection model to process the vehicle appearance photo at the corresponding angle to obtain vehicle appearance data.

Further, in the above identity recognition method provided in the embodiment of the present application, the selecting target vehicle images from the track image set at a plurality of shooting angles includes:

determining a model training angle corresponding to a pre-trained photo detection model, wherein the model training angle has a mapping relation with the shooting angle;

positioning the target vehicle according to a target angle serving as a reference angle, and calculating an angle difference value between the model training angle and the target angle;

and selecting the vehicle image corresponding to the angle difference as a target vehicle image of a plurality of shooting angles corresponding to the model training angles.

Further, in the above identity recognition method provided in the embodiment of the present application, the detecting whether the vehicle characteristic data and the person characteristic data match includes:

acquiring the vehicle characteristic data;

traversing a preset mapping relation between the vehicle characteristics and the personnel characteristics according to the vehicle characteristic data to obtain target personnel characteristic data;

detecting whether the target person characteristic data is consistent with the person characteristic data;

when the detection result is that the target person characteristic data is consistent with the person characteristic data, determining that the vehicle characteristic data is matched with the person characteristic data;

and when the detection result is that the target person characteristic data is inconsistent with the person characteristic data, determining that the vehicle characteristic data is not matched with the person characteristic data.

Further, in the above identity recognition method provided in the embodiment of the present application, the method further includes:

acquiring the vehicle license plate data;

traversing a preset mapping relation between the vehicle license plate and the vehicle appearance according to the vehicle license plate data to obtain target vehicle appearance data;

detecting whether the target vehicle appearance data is consistent with the vehicle appearance data;

when the detection result is that the target vehicle appearance data is consistent with the vehicle appearance data, determining that the vehicle license plate data is matched with the vehicle appearance data;

and when the detection result is that the target vehicle appearance data is inconsistent with the vehicle appearance data, determining that the vehicle number plate data is not matched with the vehicle appearance data.

Further, in the above identity recognition method provided in this embodiment of the present application, before the capturing the video image of the target vehicle in the specific area, the method further includes:

acquiring basic information of a pre-purchased vehicle, automatically analyzing the basic information based on a pre-trained vehicle standard judgment model to judge that the pre-purchased vehicle meets the national vehicle standard, and generating a target two-dimensional code aiming at the basic information;

determining personnel information of the pre-purchased vehicle, and detecting whether the personnel information is correct based on a face recognition technology;

when the detection result is that the personnel information is correct, determining the basic information of the pre-purchased vehicle through the target two-dimensional code, and binding the personnel information with the basic information to generate a vehicle number plate;

after the pre-purchased vehicle is provided with the vehicle number plate, vehicle photos of a plurality of shooting angles corresponding to the pre-purchased vehicle are obtained, and the vehicle photos are input into a photo detection model corresponding to the shooting angles so as to judge that the vehicle photos meet the photo detection requirements.

A second aspect of the embodiments of the present application further provides an identity recognition apparatus, including:

the video acquisition module is used for acquiring a video image of a target vehicle in a specific area;

the video processing module is used for preprocessing the video image to obtain a vehicle photo and a face photo of the target vehicle, wherein the vehicle photo comprises a vehicle number plate photo and a plurality of angles of vehicle appearance photos;

the angle calculation module is used for calculating the shooting angle of the vehicle appearance photo and inquiring the mapping relation between the preset shooting angle and the photo detection model according to the shooting angle to obtain a target photo detection model;

the vehicle data acquisition module is used for calling the target photo detection model to process the vehicle number plate photo and the vehicle appearance photo corresponding to the shooting angle to obtain vehicle characteristic data, wherein the vehicle characteristic data comprises vehicle number plate data and vehicle appearance data;

the personnel data acquisition module is used for calling a face recognition model to process the face photo to obtain personnel characteristic data;

the data detection module is used for detecting whether the vehicle characteristic data is matched with the personnel characteristic data;

and the warning prompt module is used for outputting warning prompts when the detection result is that the vehicle characteristic data is not matched with the personnel characteristic data.

A third aspect of embodiments of the present application further provides a computer device, where the computer device includes a processor, and the processor is configured to implement the identity recognition method according to any one of the above when executing a computer program stored in a memory.

The fourth aspect of the embodiments of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the identity recognition method described in any one of the above.

According to the identity recognition method, the identity recognition device, the computer equipment and the computer readable storage medium provided by the embodiment of the application, the video image of the target vehicle in the specific area is collected, the video is preprocessed, and the vehicle photo and the face photo of the target vehicle are obtained, wherein the vehicle photo comprises the vehicle number plate photo and the vehicle appearance photo of a plurality of shooting angles, so that the matching of the personnel, the license plate and the vehicle appearance of the target vehicle is realized, and the condition that the use of the electric bicycle conforms to the regulation of one person for one license plate is determined; in addition, when this application aims at handling the vehicle photo, select the photo detection model that corresponds the vehicle and shoot the angle and accomplish the photo and handle for the vehicle photo of specific angle can call suitable photo detection model, improves the accuracy that the photo was handled, improves electric bicycle identification's accuracy then. This application can be applied to in each functional module in wisdom cities such as wisdom government affairs, wisdom traffic, for example the identity module of wisdom government affairs etc. can promote the rapid development in wisdom city.

Drawings

Fig. 1 is a flowchart of an identity recognition method according to an embodiment of the present application.

Fig. 2 is a structural diagram of an identification apparatus according to a second embodiment of the present application.

Fig. 3 is a schematic structural diagram of a computer device provided in the third embodiment of the present application.

The following detailed description will further illustrate the present application in conjunction with the above-described figures.

Detailed Description

In order that the above objects, features and advantages of the present application can be more clearly understood, a detailed description of the present application will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.

In the following description, numerous specific details are set forth to provide a thorough understanding of the present application, and the described embodiments are a part, but not all, of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

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 application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

The identity recognition method provided by the embodiment of the invention is executed by computer equipment, and correspondingly, the identity recognition device runs in the computer equipment.

Fig. 1 is a flowchart of an identification method according to a first embodiment of the present application. The identification method can be used for identifying the identity of a vehicle, and the vehicle can be an electric bicycle and the like, and is not limited herein. As shown in fig. 1, the identification method may include the following steps, and the order of the steps in the flowchart may be changed and some steps may be omitted according to different requirements:

and S11, acquiring a video image of the target vehicle in the specific area.

In at least one embodiment of the present application, when identifying the identity of the electric bicycle, an electric bicycle basic database needs to be established first, where the electric bicycle basic database is used to store information of a plurality of electric bicycles, such as people, vehicles, and license plates, and for an electric bicycle, it should satisfy that one electric bicycle has one person and one license plate. Optionally, the establishing an electric bicycle base database includes:

acquiring basic information of a pre-purchased vehicle, automatically analyzing the basic information based on a pre-trained vehicle standard judgment model to judge that the pre-purchased vehicle meets the national vehicle standard, and generating a target two-dimensional code aiming at the basic information;

determining personnel information of the pre-purchased vehicle, and detecting whether the personnel information is correct based on a face recognition technology;

when the detection result is that the personnel information is correct, determining the basic information of the pre-purchased vehicle through the target two-dimensional code, and binding the personnel information with the basic information to generate a vehicle number plate;

after the pre-purchased vehicle is provided with the vehicle number plate, a vehicle photo of a preset angle corresponding to the pre-purchased vehicle is obtained, and the vehicle photo is input into a photo detection model corresponding to the preset angle so as to judge that the vehicle photo meets the photo detection requirement.

Wherein, the person who buys the vehicle can input the name and the identification card number, upload the front side of the identification card, the back side of the identification card and the head portrait information. The method for detecting whether the information of the personnel is correct based on the face recognition technology mainly comprises the following two steps:

firstly, performing OCR recognition on the front surface of an uploaded identity card, recognizing a name and an identity card number, comparing the recognized name and identity card number with a name and an identity card input by a user, and if the recognized name and identity card number are consistent with the name and identity card input by the user, passing verification;

secondly, a large number of photos of the big head of the face and the photos of the identity card are utilized for labeling, the positions of the face on the photos are marked, and data modeling, training and deep learning are carried out, so that the face can be accurately detected from the photos. And a large number of face photos are utilized to extract multiple features of the face part, and a large number of training is carried out in the feature extraction process. And performing multi-feature similarity calculation by using two pictures of the same person marked in advance, training by using a large number of two pictures of the same person in the similarity calculation process to obtain higher similarity of the same person, and judging that the person is the same person if the similarity is greater than a certain threshold value.

The step of determining the basic information of the pre-purchased vehicle through the target two-dimensional code, and binding the personnel information with the basic information to generate the vehicle number plate may include the steps of:

and the unique two-dimensional code of the vehicle is utilized to automatically bring out the information of the vehicle and enter a record page. On the filing page, the user needs to supplement user information, after the information needed by filing is submitted to the system, the system compares the personnel information by using a pre-trained algorithm, if the personnel information comparison is completely correct, the system automatically checks and passes, and automatically generates a number plate according to the type of the vehicle. The vehicle number plate is the only information used for identifying the identity of the electric bicycle.

The preset angle is a preset angle used for identifying the appearance information of the electric bicycle, and illustratively can be 4 photos such as a whole vehicle coded photo, a vehicle front 45-degree photo, a vehicle rear 45-degree photo and a license plate photo.

In at least one embodiment of the present application, the specific area is an area where a plurality of electronic monitors are installed, the electronic monitors are used for monitoring video images of electric bicycles running in the area, and the video images include information of people driving the electric bicycles, information of vehicles, and information of license plates. The number of the target vehicles may be 1, or may be multiple, and is not limited herein.

S12, preprocessing the video image to obtain a vehicle photo and a face photo of the target vehicle, wherein the vehicle photo comprises a vehicle number plate photo and a vehicle appearance photo of a plurality of shooting angles.

In at least one embodiment of the application, the video image comprises personnel information, vehicle information and license plate information for driving an electric bicycle, the video image is preprocessed and split into personnel video images, vehicle video images and license plate video images, and the personnel video images are respectively subjected to feature extraction to obtain face photos; extracting the characteristics of the vehicle video image to obtain vehicle appearance pictures at a plurality of shooting angles; and extracting the characteristics of the license plate video image to obtain a vehicle license plate photo.

Optionally, the preprocessing the video image to obtain the vehicle picture and the face picture of the target vehicle includes:

splitting the video image according to a preset frame rate to obtain a track image set consisting of a plurality of frame track images;

detecting whether a face image and a license plate image exist in the track image set;

when the detection result is that the face images and the license plate images exist in the track image set, selecting a target face image and a target license plate image of which the image definition is greater than a preset definition threshold;

selecting target vehicle images of a plurality of shooting angles from the track image set;

and combining the target face image, the target license plate image and the target vehicle image to obtain a target image set.

The preset frame rate refers to a preset frequency for extracting a plurality of frames of images in the video images, the video images are extracted through the preset frame rate to obtain a plurality of track images, then vehicle recognition or face recognition is carried out on the track images to obtain vehicle photos and face photos, and the photo obtaining rate and accuracy can be improved.

Optionally, the selecting the target vehicle image at a plurality of shooting angles from the track image set includes:

determining a model training angle corresponding to a pre-trained photo detection model, wherein the model training angle has a mapping relation with the shooting angle;

positioning the target vehicle according to a target angle serving as a reference angle, and calculating an angle difference value between the model training angle and the target angle;

and selecting the vehicle image corresponding to the angle difference as a target vehicle image of a plurality of shooting angles corresponding to the model training angles.

The number of the photo detection models is multiple, and each photo detection model is used for detecting vehicle photos from a plurality of shooting angles to obtain vehicle characteristic data. The shooting angle is a preset angle for identifying the appearance information of the electric bicycle, and illustratively, the shooting angle can be 4 photos such as a whole bicycle coded photo, a vehicle front 45-degree photo, a vehicle rear 45-degree photo, a license plate photo and the like. And training the photo detection model by taking the four angles as an example, preparing a large number of photos of four scenes, namely a whole vehicle coded photo, a 45-degree photo in front of the vehicle, a 45-degree photo in back of the vehicle and a famous photo in advance, and performing data modeling, training and deep learning on the 4 scenes respectively to obtain the photo detection model capable of detecting the 4 scenes. And the model training angle and the shooting angle have a mapping relation. The target angle refers to a preset angle for referring to a shooting angle.

And S13, calculating the shooting angle of the vehicle appearance picture, and inquiring the mapping relation between the preset shooting angle and the picture detection model according to the shooting angle to obtain the target picture detection model.

In at least one embodiment of the application, for the vehicle appearance photos of a plurality of selected shooting angles, a preset tag is added for identifying the shooting angles of the vehicle appearance photos, and the shooting angles of the vehicle appearance photos can be obtained by inquiring the preset tag carried by each vehicle appearance photo. The preset tag may be a digital tag, and the like, which is not limited herein.

And S14, calling the target photo detection model to process the vehicle number plate photo and the vehicle appearance photo corresponding to the shooting angle to obtain vehicle characteristic data, wherein the vehicle characteristic data comprises vehicle number plate data and vehicle appearance data.

In at least one embodiment of the present application, the photo detection model is invoked to process the vehicle photo to obtain vehicle characteristic data, where the vehicle characteristic data includes vehicle number plate data and vehicle appearance data.

Optionally, the calling the target photo detection model to process the vehicle license plate photo and the vehicle appearance photo corresponding to the shooting angle to obtain vehicle feature data includes:

acquiring a license plate region in the vehicle license plate photo, and performing feature extraction on feature data of the license plate region to obtain vehicle license plate data;

acquiring the vehicle appearance photo, and calculating the shooting angle of the vehicle appearance photo;

and determining a target photo detection model corresponding to the shooting angle, and calling the target detection model to process the vehicle appearance photo at the corresponding angle to obtain vehicle appearance data.

And S15, calling a face recognition model to process the face picture to obtain the personnel feature data.

In at least one embodiment of the present application, the person feature data refers to facial feature data of a person, for example, width data of a human face, position data of five sense organs, shape data of five sense organs, and the like, and by analyzing the person feature data, identity information of the person can be obtained to determine whether the person is a person matched with vehicle feature data stored in a basic database.

Optionally, the calling a face recognition model to process the face picture, and obtaining the person feature data includes:

positioning a face area in the face photo;

extracting target feature data of the face region by using features;

and storing the target characteristic data according to a preset data format to obtain the personnel characteristic data.

The target feature data refers to data such as width data of a human face of a person, position data of five sense organs, shape data of five sense organs, and the like, and the preset data format refers to a preset format for storing a plurality of target feature data, which is not limited herein.

In an embodiment, there may be a case where a human face is occluded (for example, a case with a mask or a helmet), and when the human face is detected to be occluded, the person feature data may also be obtained by comprehensively analyzing the body feature and the clothing feature of the person driving the target vehicle. Optionally, when it is detected that the face is occluded, the method further includes:

preprocessing the video image to obtain a behavior video corresponding to the user, and selecting the body data and the clothing data of the user from the behavior video; calling a pre-trained body calculation model to process the body data to obtain body characteristics;

calling a pre-trained clothes calculation model to process the clothes data to obtain clothes characteristics;

and determining personnel characteristic data of the user according to the physical characteristics and the clothing characteristics.

The behavior video refers to a video of a user when driving a target vehicle, the behavior video includes body data and clothing data of the user, and the body data may refer to a ratio of lengths of various parts of the user, for example, data such as a shoulder width ratio; the dress data may be a style of dress (e.g., dress, etc.), a color, etc. The person feature data may be gender data and face data of the user obtained according to the body feature and the clothing feature. It can be understood that a fine-grained matrix of the body features, the clothing features and the face features is established in advance, and the face features corresponding to the body features and the clothing features can be obtained by querying the fine-grained matrix.

And S16, detecting whether the vehicle characteristic data and the personnel characteristic data are matched, and executing the step S17 when the detection result is that the vehicle characteristic data and the personnel characteristic data are not matched.

In at least one embodiment of the present application, it is detected whether the vehicle feature data matches the person feature data, that is, relevant information consistent with the vehicle feature data is obtained from a basic database, where the relevant information includes vehicle license plate information, vehicle appearance information, and information of purchasing persons and persons. The vehicle number plate information, the vehicle appearance information and the information of the person purchasing personnel are in one-to-one correspondence mapping relation so as to meet the requirement of 'one person one vehicle one plate' specified by the state.

Optionally, the detecting whether the vehicle characteristic data and the person characteristic data match includes:

acquiring the vehicle characteristic data;

traversing a preset mapping relation between the vehicle characteristics and the personnel characteristics according to the vehicle characteristic data to obtain target personnel characteristic data;

detecting whether the target person characteristic data is consistent with the person characteristic data;

when the detection result is that the target person characteristic data is consistent with the person characteristic data, determining that the vehicle characteristic data is matched with the person characteristic data;

and when the detection result is that the target person characteristic data is inconsistent with the person characteristic data, determining that the vehicle characteristic data is not matched with the person characteristic data.

Wherein the vehicle characteristic data comprises vehicle number plate data and vehicle appearance data, and before the detecting whether the vehicle characteristic data and the person characteristic data are matched, the method further comprises: detecting whether the vehicle license plate data matches the vehicle appearance data. Optionally, the detecting whether the vehicle license plate data and the vehicle appearance data match comprises:

acquiring the vehicle license plate data;

traversing a preset mapping relation between the vehicle license plate and the vehicle appearance according to the vehicle license plate data to obtain target vehicle appearance data;

detecting whether the target vehicle appearance data is consistent with the vehicle appearance data;

when the detection result is that the target vehicle appearance data is consistent with the vehicle appearance data, determining that the vehicle license plate data is matched with the vehicle appearance data;

and when the detection result is that the target vehicle appearance data is inconsistent with the vehicle appearance data, determining that the vehicle number plate data is not matched with the vehicle appearance data.

And S17, outputting an alarm prompt.

In at least one embodiment of the application, when the detection result is that the vehicle characteristic data is matched with the person characteristic data, determining that the target vehicle meets the regulation of one person for one license plate; and outputting an alarm prompt when the detection result is that the vehicle characteristic data is not matched with the personnel characteristic data.

According to the identity recognition method provided by the embodiment of the application, the video image of the target vehicle in the specific area is collected, the video is preprocessed, and the vehicle photo and the face photo of the target vehicle are obtained, wherein the vehicle photo comprises the vehicle number plate photo and the vehicle appearance photo of a plurality of shooting angles, so that the matching of the personnel, the license plate and the vehicle appearance of the target vehicle is realized, and the fact that the electric bicycle is used according with the rule that one person is used and one license plate is used is determined; in addition, when this application aims at handling the vehicle photo, select the photo detection model that corresponds the vehicle photo angle and accomplish the photo and handle for the vehicle photo of specific angle can call suitable photo detection model, improves the accuracy that the photo was handled, improves electric bicycle identification's accuracy then. This application can be applied to in each functional module in wisdom cities such as wisdom government affairs, wisdom traffic, for example the identity module of wisdom government affairs etc. can promote the rapid development in wisdom city.

Fig. 2 is a structural diagram of an identification apparatus according to a second embodiment of the present application.

In some embodiments, the identification device 20 may include a plurality of functional modules composed of computer program segments. The computer program of each program segment in the identification device 20 may be stored in a memory of a computer device and executed by at least one processor to perform the functions of the model training process (described in detail in fig. 1).

In this embodiment, the identification device 20 may be divided into a plurality of functional modules according to the functions performed by the identification device. The functional module may include: the system comprises a video acquisition module 201, a video processing module 202, an angle calculation module 203, a vehicle data acquisition module 204, a personnel data acquisition module 205, a data detection module 206 and an alarm prompt module 207. A module as referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in a memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.

The video capture module 201 is configured to capture a video image of a target vehicle in a specific area.

In at least one embodiment of the present application, when identifying the identity of the electric bicycle, an electric bicycle basic database needs to be established first, where the electric bicycle basic database is used to store information of a plurality of electric bicycles, such as people, vehicles, and license plates, and for an electric bicycle, it should satisfy that one electric bicycle has one person and one license plate. Optionally, the establishing an electric bicycle base database includes:

acquiring basic information of a pre-purchased vehicle, automatically analyzing the basic information based on a pre-trained vehicle standard judgment model to judge that the pre-purchased vehicle meets the national vehicle standard, and generating a target two-dimensional code aiming at the basic information;

determining personnel information of the pre-purchased vehicle, and detecting whether the personnel information is correct based on a face recognition technology;

when the detection result is that the personnel information is correct, determining the basic information of the pre-purchased vehicle through the target two-dimensional code, and binding the personnel information with the basic information to generate a vehicle number plate;

after the pre-purchased vehicle is provided with the vehicle number plate, a vehicle photo of a preset angle corresponding to the pre-purchased vehicle is obtained, and the vehicle photo is input into a photo detection model corresponding to the preset angle so as to judge that the vehicle photo meets the photo detection requirement.

Wherein, the person who buys the vehicle can input the name and the identification card number, upload the front side of the identification card, the back side of the identification card and the head portrait information. The method for detecting whether the information of the personnel is correct based on the face recognition technology mainly comprises the following two steps:

firstly, performing OCR recognition on the front surface of an uploaded identity card, recognizing a name and an identity card number, comparing the recognized name and identity card number with a name and an identity card input by a user, and if the recognized name and identity card number are consistent with the name and identity card input by the user, passing verification;

secondly, a large number of photos of the big head of the face and the photos of the identity card are utilized for labeling, the positions of the face on the photos are marked, and data modeling, training and deep learning are carried out, so that the face can be accurately detected from the photos. And a large number of face photos are utilized to extract multiple features of the face part, and a large number of training is carried out in the feature extraction process. And performing multi-feature similarity calculation by using two pictures of the same person marked in advance, training by using a large number of two pictures of the same person in the similarity calculation process to obtain higher similarity of the same person, and judging that the person is the same person if the similarity is greater than a certain threshold value.

The step of determining the basic information of the pre-purchased vehicle through the target two-dimensional code, and binding the personnel information with the basic information to generate the vehicle number plate may include the steps of:

and the unique two-dimensional code of the vehicle is utilized to automatically bring out the information of the vehicle and enter a record page. On the filing page, the user needs to supplement user information, after the information needed by filing is submitted to the system, the system compares the personnel information by using a pre-trained algorithm, if the personnel information comparison is completely correct, the system automatically checks and passes, and automatically generates a number plate according to the type of the vehicle. The vehicle number plate is the only information used for identifying the identity of the electric bicycle.

The preset angle is a preset angle used for identifying the appearance information of the electric bicycle, and illustratively can be 4 photos such as a whole vehicle coded photo, a vehicle front 45-degree photo, a vehicle rear 45-degree photo and a license plate photo.

In at least one embodiment of the present application, the specific area is an area where a plurality of electronic monitors are installed, the electronic monitors are used for monitoring video images of electric bicycles running in the area, and the video images include information of people driving the electric bicycles, information of vehicles, and information of license plates. The number of the target vehicles may be 1, or may be multiple, and is not limited herein.

The video processing module 202 is configured to pre-process the video image to obtain a vehicle photo and a face photo of the target vehicle, where the vehicle photo includes a vehicle number plate photo and a vehicle appearance photo at a plurality of shooting angles.

In at least one embodiment of the application, the video image comprises personnel information, vehicle information and license plate information for driving an electric bicycle, the video image is preprocessed and split into personnel video images, vehicle video images and license plate video images, and the personnel video images are respectively subjected to feature extraction to obtain face photos; extracting the characteristics of the vehicle video image to obtain vehicle appearance pictures at a plurality of shooting angles; and extracting the characteristics of the license plate video image to obtain a vehicle license plate photo.

Optionally, the preprocessing the video image to obtain the vehicle picture and the face picture of the target vehicle includes:

splitting the video image according to a preset frame rate to obtain a track image set consisting of a plurality of frame track images;

detecting whether a face image and a license plate image exist in the track image set;

when the detection result is that the face images and the license plate images exist in the track image set, selecting a target face image and a target license plate image of which the image definition is greater than a preset definition threshold;

selecting target vehicle images of a plurality of shooting angles from the track image set;

and combining the target face image, the target license plate image and the target vehicle image to obtain a target image set.

The preset frame rate refers to a preset frequency for extracting a plurality of frames of images in the video images, the video images are extracted through the preset frame rate to obtain a plurality of track images, then vehicle recognition or face recognition is carried out on the track images to obtain vehicle photos and face photos, and the photo obtaining rate and accuracy can be improved.

Optionally, the selecting the target vehicle image at a plurality of shooting angles from the track image set includes:

determining a model training angle corresponding to a pre-trained photo detection model, wherein the model training angle has a mapping relation with the shooting angle;

positioning the target vehicle according to a target angle serving as a reference angle, and calculating an angle difference value between the model training angle and the target angle;

and selecting the vehicle image corresponding to the angle difference as a target vehicle image of a plurality of shooting angles corresponding to the model training angles.

The number of the photo detection models is multiple, and each photo detection model is used for detecting vehicle photos from a plurality of shooting angles to obtain vehicle characteristic data. The shooting angle is a preset angle for identifying the appearance information of the electric bicycle, and illustratively, the shooting angle can be 4 photos such as a whole bicycle coded photo, a vehicle front 45-degree photo, a vehicle rear 45-degree photo, a license plate photo and the like. And training the photo detection model by taking the four angles as an example, preparing a large number of photos of four scenes, namely a whole vehicle coded photo, a 45-degree photo in front of the vehicle, a 45-degree photo in back of the vehicle and a famous photo in advance, and performing data modeling, training and deep learning on the 4 scenes respectively to obtain the photo detection model capable of detecting the 4 scenes. And the model training angle and the shooting angle have a mapping relation. The target angle refers to a preset angle for referring to a shooting angle.

The angle calculation module 203 is configured to calculate a shooting angle of the vehicle appearance photo, and query a mapping relationship between a preset shooting angle and a photo detection model according to the shooting angle to obtain a target photo detection model.

In at least one embodiment of the application, for the vehicle appearance photos of a plurality of selected shooting angles, a preset tag is added for identifying the shooting angles of the vehicle appearance photos, and the shooting angles of the vehicle appearance photos can be obtained by inquiring the preset tag carried by each vehicle appearance photo. The preset tag may be a digital tag, and the like, which is not limited herein.

The vehicle data acquisition module 204 is configured to invoke the target photo detection model to process the vehicle license plate photo and the vehicle appearance photo corresponding to the shooting angle, so as to obtain vehicle characteristic data, where the vehicle characteristic data includes vehicle license plate data and vehicle appearance data.

In at least one embodiment of the present application, the photo detection model is invoked to process the vehicle photo to obtain vehicle characteristic data, where the vehicle characteristic data includes vehicle number plate data and vehicle appearance data.

Optionally, the calling the target photo detection model to process the vehicle license plate photo and the vehicle appearance photo corresponding to the shooting angle to obtain vehicle feature data includes:

acquiring a license plate region in the vehicle license plate photo, and performing feature extraction on feature data of the license plate region to obtain vehicle license plate data;

acquiring the vehicle appearance photo, and calculating the shooting angle of the vehicle appearance photo;

and determining a target photo detection model corresponding to the shooting angle, and calling the target detection model to process the vehicle appearance photo at the corresponding angle to obtain vehicle appearance data.

The personnel data obtaining module 205 is configured to invoke a face recognition model to process the face picture, so as to obtain personnel feature data.

In at least one embodiment of the present application, the person feature data refers to facial feature data of a person, for example, width data of a human face, position data of five sense organs, shape data of five sense organs, and the like, and by analyzing the person feature data, identity information of the person can be obtained to determine whether the person is a person matched with vehicle feature data stored in a basic database.

Optionally, the calling a face recognition model to process the face picture, and obtaining the person feature data includes:

positioning a face area in the face photo;

extracting target feature data of the face region by using features;

and storing the target characteristic data according to a preset data format to obtain the personnel characteristic data.

The target feature data refers to data such as width data of a human face of a person, position data of five sense organs, shape data of five sense organs, and the like, and the preset data format refers to a preset format for storing a plurality of target feature data, which is not limited herein.

In an embodiment, there may be a case where a human face is occluded (for example, a case with a mask or a helmet), and when the human face is detected to be occluded, the person feature data may also be obtained by comprehensively analyzing the body feature and the clothing feature of the person driving the target vehicle. Optionally, when it is detected that the face is occluded, the personnel data acquisition module 205 further includes:

preprocessing the video image to obtain a behavior video corresponding to the user, and selecting the body data and the clothing data of the user from the behavior video; calling a pre-trained body calculation model to process the body data to obtain body characteristics;

calling a pre-trained clothes calculation model to process the clothes data to obtain clothes characteristics;

and determining personnel characteristic data of the user according to the physical characteristics and the clothing characteristics.

The behavior video refers to a video of a user when driving a target vehicle, the behavior video includes body data and clothing data of the user, and the body data may refer to a ratio of lengths of various parts of the user, for example, data such as a shoulder width ratio; the dress data may be a style of dress (e.g., dress, etc.), a color, etc. The person feature data may be gender data and face data of the user obtained according to the body feature and the clothing feature. It can be understood that a fine-grained matrix of the body features, the clothing features and the face features is established in advance, and the face features corresponding to the body features and the clothing features can be obtained by querying the fine-grained matrix.

The data detection module 206 is configured to detect whether the vehicle characteristic data matches the person characteristic data.

In at least one embodiment of the present application, it is detected whether the vehicle feature data matches the person feature data, that is, relevant information consistent with the vehicle feature data is obtained from a basic database, where the relevant information includes vehicle license plate information, vehicle appearance information, and information of purchasing persons and persons. The vehicle number plate information, the vehicle appearance information and the information of the person purchasing personnel are in one-to-one correspondence mapping relation so as to meet the requirement of 'one person one vehicle one plate' specified by the state.

Optionally, the detecting whether the vehicle characteristic data and the person characteristic data match includes:

acquiring the vehicle characteristic data;

traversing a preset mapping relation between the vehicle characteristics and the personnel characteristics according to the vehicle characteristic data to obtain target personnel characteristic data;

detecting whether the target person characteristic data is consistent with the person characteristic data;

when the detection result is that the target person characteristic data is consistent with the person characteristic data, determining that the vehicle characteristic data is matched with the person characteristic data;

and when the detection result is that the target person characteristic data is inconsistent with the person characteristic data, determining that the vehicle characteristic data is not matched with the person characteristic data.

Wherein the vehicle characteristic data includes vehicle number plate data and vehicle appearance data, and before the detecting whether the vehicle characteristic data is matched with the person characteristic data, the data detecting module 206 further includes: detecting whether the vehicle license plate data matches the vehicle appearance data. Optionally, the detecting whether the vehicle license plate data and the vehicle appearance data match comprises:

acquiring the vehicle license plate data;

traversing a preset mapping relation between the vehicle license plate and the vehicle appearance according to the vehicle license plate data to obtain target vehicle appearance data;

detecting whether the target vehicle appearance data is consistent with the vehicle appearance data;

when the detection result is that the target vehicle appearance data is consistent with the vehicle appearance data, determining that the vehicle license plate data is matched with the vehicle appearance data;

and when the detection result is that the target vehicle appearance data is inconsistent with the vehicle appearance data, determining that the vehicle number plate data is not matched with the vehicle appearance data.

And the warning prompt module 207 is used for outputting a warning prompt when the detection result is that the vehicle characteristic data is not matched with the personnel characteristic data.

In at least one embodiment of the application, when the detection result is that the vehicle characteristic data is matched with the person characteristic data, determining that the target vehicle meets the regulation of one person for one license plate; and outputting an alarm prompt when the detection result is that the vehicle characteristic data is not matched with the personnel characteristic data.

Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present application. In the preferred embodiment of the present application, the computer device 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34.

It will be appreciated by those skilled in the art that the configuration of the computer device shown in fig. 3 is not a limitation of the embodiments of the present application, and may be a bus-type configuration or a star-type configuration, and that the computer device 3 may include more or less hardware or software than those shown, or a different arrangement of components.

In some embodiments, the computer device 3 is a device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The computer device 3 may also include a client device, which includes, but is not limited to, any electronic product capable of interacting with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, etc.

It should be noted that the computer device 3 is only an example, and other existing or future electronic products, such as those that may be adapted to the present application, are also included in the scope of the present application and are incorporated herein by reference.

In some embodiments, the memory 31 has stored therein a computer program which, when executed by the at least one processor 32, performs all or part of the steps of the identification method as described. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

In some embodiments, the at least one processor 32 is a Control Unit (Control Unit) of the computer device 3, connects various components of the entire computer device 3 by using various interfaces and lines, and executes various functions and processes data of the computer device 3 by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31. For example, the at least one processor 32, when executing the computer program stored in the memory, implements all or part of the steps of the identification method described in the embodiments of the present application; or to implement all or part of the functionality of the identification device. The at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips.

In some embodiments, the at least one communication bus 33 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.

Although not shown, the computer device 3 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The computer device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present application may be integrated into one processing 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 in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the specification may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application.