WO2020192122A1

WO2020192122A1 - Off-site law enforcement picture intelligent auditing method and system for vehicles running red light

Info

Publication number: WO2020192122A1
Application number: PCT/CN2019/115220
Authority: WO
Inventors: 吕伟韬; 周东; 陈凝; 潘阳阳
Original assignee: 江苏智通交通科技有限公司
Priority date: 2019-03-27
Filing date: 2019-11-04
Publication date: 2020-10-01
Also published as: CN110009913B; CN110009913A

Abstract

An off-site law enforcement picture intelligent auditing method and system for vehicles running a red light. The system comprises an accessing module, a labeling module, a picture pre-processing module, a deep-learning module, a vehicle positioning module and an illegality intelligent auditing module. The method comprises the following steps: accessing, by means of the accessing module, an off-site law enforcement system to collect illegal information of a vehicle running a red light; obtaining, by means of the deep-learning module, the position of a target vehicle to be audited and a contour positioning mark; generating, by means of centroid coordinates of the contour positioning mark, a travel track of the target vehicle; marking, by means of the vehicle positioning module, the positions of a stop line and a boundary line of the target vehicle; determining, according to a position relationship between the travel track of the target vehicle and the stop line of an entrance road, the traveling means of the target vehicle and whether an illegal behavior of running the red light occurs in the travel process; and identifying, according to a signal light of the target vehicle and phase information thereof, the validity of an illegal record of the target vehicle running the red light. The auditing efficiency thereof is thus greatly improved.

Description

Method and system for intelligently reviewing off-site law enforcement pictures of vehicles running red lights

Technical field

The invention belongs to the technical field of traffic data processing, and specifically relates to a method and system for intelligently reviewing off-site law enforcement pictures of vehicles running red lights.

Background technique

Intelligent traffic violation monitoring and photo management system, commonly known as "electronic police" and "electronic eye", is the main equipment for off-site law enforcement in current road traffic. According to the requirements of the standard "General Technical Requirements for Automatic Red Light Recording System (GA/T496-2014)", The electronic police will automatically capture three pictures when it detects a vehicle crossing a red light, as a basis for law enforcement, including: (a) a picture that reflects that the vehicle has not reached the stop line; (b) a picture that reflects that the vehicle has crossed the stop line Picture, (c) a picture that can reflect the forward displacement of the vehicle and (b) the picture; as a picture record, the standard also requires that the final picture recorded should be combined into a picture file, and at least include: time, place, Information such as directions, lanes, and equipment numbers. Usually the electronic police system synthesizes a picture with a close-up of the license plate and the above three pictures, and then the reviewer retrieves the records for manual review of the pictures, and the illegal records confirmed by the manual review will be uploaded to the "six in one" illegal database .

In reality, due to factors such as the working status of electronic equipment, communication quality, and environmental interference, off-site law enforcement must verify the original data captured by the electronic police to ensure the rigor of law enforcement. However, in the prior art, the number of off-site law enforcement records is huge, and quality screening by relying on manual labor is low in efficiency and prone to errors and omissions.

Summary of the invention

The purpose of the present invention is to propose a method and system for intelligently reviewing off-site law enforcement pictures of vehicles running red lights to solve the problem of low efficiency and error-prone problems in the prior art due to manual inspection of off-site law enforcement records quality. Specific technical solutions as follows:

On the one hand, an off-site law enforcement image intelligent review system for vehicles running red lights is provided. The system includes a docking module, an annotation module, an image preprocessing module, a deep learning module, a vehicle positioning module, and an illegal intelligence review module, wherein:

Docking module: access to the off-site law enforcement system through the designated data interface, and extract the vehicle’s red light violation information from the off-site law enforcement system;

Annotation module: Obtain the red light violation information from the docking module, provide the user with a selection annotation tool through the interactive interface, and based on the user's annotation status through the selection annotation tool in the original composite picture corresponding to the red light violation information , Output the attribute information of the captured image corresponding to the specified capture device;

Picture preprocessing module: Obtain the original synthesized picture of the captured vehicle running through the red light from the docking module, and split the original synthesized picture into a first picture P1, a second picture P2, and a third picture P3 based on GA/T496-2014 requirements;

Deep learning module: based on the attribute information of the target vehicle and the deep learning algorithm, perform contour recognition of the motor vehicles in the P1, P2 and P3, and perform the light color recognition and phase recognition of the target signal lamp based on the P1, P2 and P3; According to the results of light color recognition and phase recognition, it is judged whether the illegal detection record of the target vehicle can be intelligently reviewed. If it cannot be reviewed, the recognition result is transmitted to the illegal intelligent review module; otherwise, the recognition result is transmitted To the vehicle positioning module for further processing;

Vehicle positioning module: access the red light violation information of the docking module, identify the location of the target vehicle in P1 and mark the contour, and use the target tracking algorithm CSRT to mark the contour of the target vehicle in P2 and P3;

Illegal intelligence review module: access to the light color and phase recognition results of the deep learning module, as well as the contour mark of the target vehicle of the vehicle positioning module, and the P1, P2, and P3 split by the image preprocessing module, which are used for violations in the off-site law enforcement system The inspection records are reviewed to determine whether the target vehicle has red light running behavior.

Further, the information on the violation of red light running includes the original composite picture and vehicle number plate number data;

The interactive interface is set in terminals such as computers, mobile phones, and IPADs;

The selection and labeling tools include rectangular frame labeling, polygonal frame labeling and multi-line segment labeling.

Further, the attribute information includes attribute information of a static target and attribute information of a dynamic target, wherein the static target includes a stop line of an entrance and exit, a boundary line of a left turn, a boundary line of a right turn, and a signal light. The attribute information of the stop line, the left-turn boundary line, and the right-turn boundary line are the polyline type and its position information, the attribute information of the signal light is the rectangular frame type and its lamp head position information, and the rectangular frame type includes the left-turn arrow light , Straight arrow light, right turn arrow light, left round pie light, straight round pie light, straight left round pie light, U-turn arrow light, special bus lights; the dynamic target is the vehicle in the original composite picture, passing The selection marking tool marks the outline of the vehicle, and outputs the vehicle attribute data.

Further, the picture preprocessing module further includes: positioning the static target in the P1, P2, and P3 based on the attribute information of the static target.

Furthermore, in the deep learning module,

Obtain a vehicle recognition model based on the vehicle attribute data and deep learning algorithm training; and

The color and phase recognition model of the signal lamp is obtained through training based on the attribute information of the signal lamp.

On the other hand, an intelligent review method for off-site law enforcement pictures of vehicles running red lights is provided, which is applied to the above system. The method uses deep learning methods to identify dynamic targets in the pictures, and combines target tracking algorithms to achieve the positioning and trajectory of target vehicles. To realize the intelligent review of red light running behavior, the specific steps include:

S1. The access module is connected to the off-site law enforcement system to collect information about the violation of the vehicle running red light, and the original synthesized picture is split according to GA/T496-2014 requirements to obtain P1, P2, and P3;

S2. The deep learning module respectively identifies the location of the target vehicle whose red light running behavior is to be reviewed from P1, P2, and P3, and marks the contour of the vehicle;

S3. Determine the centroid coordinates of the contour positioning marks in P1, P2, and P3, respectively, and connect the three centroids in P1 according to the order of the pictures to generate the driving trajectory of the target vehicle;

S4. Mark the stop line and the boundary line position of the target vehicle in P1 based on the position information of the stop line, left turn boundary, and right turn boundary of the entrance lane;

S5. Detect whether there is an intersection between the travel trajectory and the stop line of the entrance road, if so, go to step S6; otherwise, detect whether the center of mass coordinates of P1 pass the stop line of the entrance road in the forward direction of the target vehicle; if so; , Then go to step S6; if not, determine that the target vehicle does not violate the law by running a red light, and end the process;

S6. Determine and obtain the turning behavior of the target vehicle at the entrance lane according to the positional relationship between the driving track and the position information of the left turn boundary line and the right turn boundary line;

S7. Based on the lamp head position information, use a deep learning algorithm to identify the signal lights and phase information of the target vehicle in P1 and P3. If the phases are the same, go to step S8; otherwise, the validity of the red light violation record corresponding to the target vehicle cannot be determined Sex and end the process;

S8. Based on the determination result of the steering behavior and the recognition result of the signal light and its phase information, if the determination result is consistent with the recognition result, it is determined that the target vehicle does not have red light running behavior; otherwise, it is determined that the target vehicle is The illegal record of running a red light is valid.

Further, step S2 includes:

S21. For P1, use a deep learning algorithm to detect all vehicles in P1 and perform contour mark positioning on the image area where the vehicles are located; extract vehicle number plate number data from the illegal information, and determine the number of vehicles in P1 according to the license plate recognition algorithm Mark the location of the corresponding target vehicle number plate; combine the vehicle area and the position of the target vehicle number plate to determine the target vehicle position in P1, and realize the location marking of the initial position contour of the illegal vehicle to be reviewed;

S22. Use deep learning algorithms for P2 and P3 to detect all vehicles in the picture, and perform contour mark positioning on the image area where the vehicle is located; determine the initial position of the target vehicle based on the positioning mark of the initial position contour; use the target tracking algorithm CSRT, The final positioning of the target vehicle is obtained in P2 and P3; combined with the vehicle area and target tracking results, the position of the target vehicle in P2 and P3 is determined, and its contour is marked for positioning.

Further, the turning behavior includes turning left, going straight and turning right.

Further, in step S6: if there is an intersection between the driving trajectory and the left turn boundary, it is determined that the target vehicle turns left; if there is an intersection between the driving trajectory and the right turn boundary, then it is determined that the target vehicle turns right If there is no intersection between the driving track and the right turn boundary line and the left turn boundary line, it is determined that the target vehicle is going straight.

The method and system for intelligently reviewing off-site law enforcement pictures of vehicles running red lights of the present invention can obtain three pictures that meet the requirements of GA/T496-2014 by splitting the original synthesized pictures in the off-site law enforcement system, and adopt deep learning algorithms, goals, etc. Image processing technology such as tracking algorithm is used to process the image to obtain the contour positioning mark, driving trajectory, and entrance stop line, left turn boundary, right turn boundary position information of the target vehicle, according to the driving trajectory and left turn boundary, right turn boundary line The position relationship of the position information is used to determine the actual driving state of the target vehicle, and the relationship between the target vehicle's light color and its phase is used to determine whether the illegal record of the target vehicle is consistent. Compared with the prior art, the present invention adopts multiple images for the target vehicle. The processing algorithm realizes the effectiveness of the illegal record of running a red light without manual review, which can improve the efficiency of the original inspection record review of off-site law enforcement and reduce labor costs.

Description of the drawings

FIG. 1 is a schematic flowchart of a method for intelligently reviewing off-site law enforcement pictures of a vehicle running a red light in an embodiment of the present invention;

2 is a schematic diagram of the structure composition and data transmission diagram of an off-site law enforcement picture intelligent review system using the red light vehicle in an embodiment of the present invention;

FIG. 3 is a schematic diagram of an original synthesized picture of off-site law enforcement in an embodiment using the method of the present invention;

FIG. 4 is a schematic diagram of a positioning diagram of a target vehicle in P1 in an embodiment of the method of the present invention;

FIG. 5 is a schematic diagram of a positioning diagram of a target vehicle in P2 in an embodiment of the method of the present invention;

Fig. 6 is a schematic diagram of the location of the target vehicle in P3 in an embodiment of the method of the present invention.

detailed description

In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention.

In the embodiment of the present invention, in order to realize the intelligent review of off-site law enforcement pictures of vehicles running red lights, an intelligent review system for off-site law enforcement pictures of vehicles running red lights is provided. The system includes a docking module, a labeling module, a picture preprocessing module, and deep learning. Module, vehicle positioning module and illegal intelligence review module, please refer to Figure 1 for details. The docking module is respectively connected with the image preprocessing module, labeling module and vehicle positioning module. The vehicle’s illegal record detection data is obtained through the docking module and transmitted to it The connected modules are processed; the image preprocessing module is also connected with the vehicle positioning module, the illegal intelligence review module, and the deep learning module. It can process the illegal record photos of the vehicle obtained from the docking module for subsequent processing; mark the module connection depth The learning module and the image preprocessing module; the vehicle positioning module is also connected to the illegal intelligence examination module; the deep learning module is also connected to the vehicle positioning module and the illegal intelligence examination module, including:

Docking module: Connect to the off-site law enforcement system through the designated data interface, and extract the vehicle’s red light violation information from the off-site law enforcement system.

Annotation module: Obtain the red light violation information from the docking module, provide the user with a selection annotation tool through the interactive interface, and output the designated capture device according to the user's annotation by the selected annotation tool in the original composite picture corresponding to the red light violation information For the attribute information of the captured picture, refer to Fig. 3, which is a schematic diagram of the original synthesized picture in the intelligent review process using the system of the present invention; wherein the attribute information includes the attribute information of the static target and the attribute information of the dynamic target, and the static target includes the entrance and exit. The attribute information of stop line, left turn boundary line, right turn boundary line and signal light, entrance and exit road stop line, left turn boundary line, right turn boundary line are the polyline type and its position information, and the attribute information of the signal light is rectangular frame type And its position information; the dynamic target is the vehicle in the original composite picture, and the vehicle profile is marked by selecting the annotation tool, and the vehicle attribute data is output.

Specifically, in the embodiment, the obtained red light violation information includes the original composite picture, as well as the vehicle number plate number data, entry lane stop line and left turn boundary line, right turn boundary line position information, signal lamp type and lamp head position information; the signal lamp The attribute information of is the rectangular frame type and its lamp head position information. The rectangular frame type includes left turn arrow light, straight arrow light, right turn arrow light, left turn round pie light, straight round pie light, straight left round pie light, U-turn arrow lights, dedicated bus lights; interactive interfaces are set on mobile terminals such as computers, mobile phones, IPAD, etc.; selection marking tools include rectangular boxes, polygonal boxes and multi-line segments.

Picture preprocessing module: Obtain the original synthesized picture corresponding to the captured vehicle running the red light from the docking module, and split the original synthesized picture into the first picture P1, the second picture P2 and the third picture P3 based on the requirements of GA/T496-2014; The attribute information of the target is used to locate the static target in P1, P2 and P3.

Deep learning module: Based on attribute information and deep learning algorithm training, the vehicle recognition model is obtained, and the vehicle recognition model is trained to perform vehicle recognition; and based on the positioning information of the signal lamp based on the image preprocessing module, that is, the static target positioning training obtains the signal lamp color, Phase recognition model to realize the light color recognition and phase recognition of the target signal lights in P1, P2, and P3; judge the feasibility of the intelligent audit of the audit record according to the light color recognition results of P1, P3 and the phase recognition results. If the light colors of the two pictures in P1, P2, and P3 in the illegal detection record of the target vehicle are inconsistent, it means that the intelligent review cannot be performed, and the recognition result is transmitted to the illegal intelligent review module for subsequent determination; otherwise, The recognition result is transmitted to the vehicle positioning module for further processing.

Vehicle positioning module: access the red light violation information of the docking module. First, in P1, all vehicle license plates are identified according to the number plate recognition method, and the target vehicle license plate number in the red light violation information is combined with the vehicle output by the deep learning module Contour marking situation, identify the target vehicle position in P1, and mark the target vehicle contour, see Figure 4 for details; then, use the target tracking algorithm CSRT to identify the target vehicle position in P2 and P3, and combine the output of the deep learning module For vehicle contour marking, determine the position of the target vehicle in P2 and P3, and perform contour positioning marking. For details, please refer to Figures 5 and 6; among them, the area shown by the dashed box in Figures 4, 5 and 6 is The vehicle positioning module automatically locates the result.

Illegal intelligence review module: access the light color and phase recognition results of the deep learning module, and at the same time access the contour positioning marks of the target vehicle determined by the vehicle positioning module in P1, P2 and P3, and respectively determine the centroid of the contour positioning marks, Determine the polyline coordinates of the target vehicle's driving trajectory according to the sequence; at the same time, access the image preprocessing module to locate the stop line of the entrance road, and identify the intersection of the target vehicle's driving trajectory polyline and the stop line of the entrance road; where, if the entrance road stops If the line has an intersection with the driving trajectory, the image preprocessing module will further determine the positioning of the left-turn boundary line and the right-turn boundary line of the target vehicle. If it is positioned as the left-turn boundary line of the target vehicle, and the left-turn boundary line and the driving trajectory If there is an intersection, it is determined that the target vehicle turns left; similarly, if it is positioned as the right turn boundary line of the target vehicle, and the right turn boundary line has an intersection with the driving track, then it is determined that the target vehicle turns right and travel; Or there are multiple intersections between the right-turn boundary line and the driving trajectory, and the review result of the illegal detection record corresponding to the target vehicle is unrecognizable; if there is no intersection between the left-turn boundary line or the right-turn boundary line and the driving trajectory, the target vehicle is determined to go straight.

At the same time, in the case that there is no intersection between the left-turn boundary line or the right-turn boundary line and the travel trajectory, it is also determined that the coordinates of the center of mass of the target vehicle's contour positioning mark on P1 on the travel trajectory are related to the position of the stop line of the entrance road. If the coordinates do not cross the stop line, the specific driving situation is determined according to the above method of judging the direction of the target vehicle, and the actual judgment result is used to determine whether the target vehicle has an illegal behavior of running a red light; otherwise, it is determined that the target vehicle does not have an illegal behavior of running a red light. The illegal intelligence review module determines that the review result of the illegal inspection record corresponding to the target vehicle is that there is no red light running.

In the actual operation process, it is also necessary to match the actual steering of the target vehicle with the light color and phase output in the deep learning module under the above-mentioned determination of the steering operation of the target vehicle. If the identified steering is with the corresponding light color , If the phase is inconsistent, the review result of the illegal detection record corresponding to the target vehicle is determined to be running a red light. For example, if the target vehicle corresponding to the signal light release phase is a left turn, and the steering of the target vehicle is determined to be a right turn, the target is determined The vehicle has a red light running behavior; otherwise, it is determined that the illegal detection record corresponding to the target vehicle is not running a red light.

Referring to Figure 2, in the embodiment of the present invention, there is also provided an off-site law enforcement picture intelligent review method for vehicles running red lights, which is applied to the above-mentioned off-site law enforcement picture intelligent review system for vehicles running red lights, and the method uses a deep learning method to identify dynamics in pictures Target, combined with the target tracking algorithm to realize the positioning and trajectory reconstruction of the target vehicle, and realize the intelligent review of red light running behavior. The specific steps include:

S1. The access module is connected to the off-site law enforcement system to collect information on the violation of the red light of the vehicle, and split the original synthesized picture according to the requirements of GA/T496-2014 to obtain P1, P2 and P3;

S2. The deep learning module identifies the location of the target vehicle whose red light running behavior is to be reviewed from P1, P2, and P3, and marks the contour of the vehicle; for P1, the deep learning algorithm is used to detect all vehicles in P1 and Carry out contour mark positioning on the image area where the vehicle is located; extract the vehicle license plate number data from the illegal information, and determine the location of the corresponding target vehicle license plate in P1 according to the license plate recognition algorithm, and perform mark positioning; combine the vehicle area and The position of the target vehicle number plate, determine the target vehicle position in P1, and realize the positioning mark of the initial position contour of the illegal vehicle to be reviewed; use the deep learning algorithm for P2 and P3 to detect all vehicles in the picture, and check the picture area where the vehicle is located Carry out contour mark positioning; determine the initial position of the target vehicle based on the positioning mark of the initial position contour; use the target tracking algorithm CSRT to obtain the final positioning of the specified vehicle in P2 and P3; combine the vehicle area and target tracking results to determine the target vehicle’s location Position in P2 and P3, and mark its outline.

S3. Determine the centroid coordinates of the contour positioning marks in P1, P2, and P3 respectively, and connect the three centroids in P1 according to the order of the pictures, that is, starting from the centroid coordinates of the contour positioning marks in P1, and sequentially connect the centroids of the contour positioning marks on P2 The coordinates, and the centroid coordinates of the contour positioning mark on P3, generate the driving trajectory of the target vehicle;

S4. Mark the stop line and boundary line position of the target vehicle in P1 based on the position information of the stop line of the entrance lane, the left turn boundary and the right turn boundary;

S5. Detect whether there is an intersection between the driving trajectory and the stop line of the entrance road, if so, go to step S6; otherwise, check whether the center of mass coordinates of P1 pass the stop line of the entrance road in the forward direction of the target vehicle; if yes, go to step S6; if not, it is determined that the target vehicle does not violate the law by running a red light, and the process ends;

S6. Determine and obtain the turning behavior of the target vehicle at the entrance lane according to the positional relationship between the driving trajectory and the position information of the left turn boundary and right turn boundary; that is, determine whether the target vehicle is turning left, turning right or driving straight; specifically, if driving If there is an intersection between the trajectory and the left-turn boundary, it is determined that the target vehicle turns left; if there is an intersection between the driving trajectory and the right-turn boundary, it is determined that the target vehicle turns right; if there is no intersection between the driving trajectory and the right-turn boundary or left-turn boundary, then Determine that the target vehicle is going straight.

S7. Use deep learning algorithms to identify the signal lights and phase information of the target vehicles in P1 and P3 based on the lamp head position information. If the phases are the same, go to step S8; otherwise, the validity of the red light violation record corresponding to the target vehicle cannot be judged. And end the process; specifically, in one embodiment, as shown in Figure 2 and Figure 4, the signal light corresponding to the target vehicle is a straight-going round pie lamp head, and the result of signal light recognition in P1 and P3: the straight-going round pie lights are all red lights, enter the step S8 performs the next step determination; in another embodiment, the signal light corresponding to the target vehicle is a left-turning arrow light, the signal light in P1 is a left-turning arrow-red light, and the signal light in P3 is a left-turning arrow-green light, and the phases are not consistent and cannot be distinguished. End this process.

S8. The recognition result based on the judgment result of the steering behavior and the signal light and its phase information. If the judgment result is consistent with the recognition result, it is judged that the target vehicle does not have red light running behavior; otherwise, it is judged that the red light running illegal record of the target vehicle is valid.

The above are only the preferred embodiments of the present invention, but do not limit the scope of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still comment on the foregoing specific The technical solutions described in the implementation manners are modified, or some of the technical features are equivalently replaced. Any equivalent structure made by using the contents of the specification and drawings of the present invention, directly or indirectly used in other related technical fields, is similarly within the protection scope of the present invention.

Claims

An off-site law enforcement picture intelligent review system for vehicles running red lights, characterized in that the system includes a docking module, a labeling module, a picture preprocessing module, a deep learning module, a vehicle positioning module and an illegal intelligence review module, wherein:

Docking module: access to the off-site law enforcement system through the designated data interface, and extract the vehicle’s red light violation information from the off-site law enforcement system;

Annotation module: Obtain the red light violation information from the docking module, provide the user with a selection annotation tool through the interactive interface, and based on the user's annotation status through the selection annotation tool in the original composite picture corresponding to the red light violation information , Output the attribute information of the captured image corresponding to the specified capture device;

Picture preprocessing module: Obtain the original synthesized picture of the captured vehicle running through the red light from the docking module, and split the original synthesized picture into a first picture P1, a second picture P2, and a third picture P3 based on GA/T496-2014 requirements;

Deep learning module: based on the attribute information of the target vehicle and the deep learning algorithm, perform contour recognition of the motor vehicles in the P1, P2 and P3, and perform the light color recognition and phase recognition of the target signal lamp based on the P1, P2 and P3; According to the results of light color recognition and phase recognition, it is judged whether the illegal detection record of the target vehicle can be intelligently reviewed. If it cannot be reviewed, the recognition result is transmitted to the illegal intelligent review module; otherwise, the recognition result is transmitted To the vehicle positioning module for further processing;

Vehicle positioning module: access the red light violation information of the docking module, identify the location of the target vehicle in P1 and mark the contour, and use the target tracking algorithm CSRT to mark the contour of the target vehicle in P2 and P3;

Illegal intelligence review module: access to the light color and phase recognition results of the deep learning module, as well as the contour mark of the target vehicle of the vehicle positioning module, and the P1, P2, and P3 split by the image preprocessing module, which are used for violations in the off-site law enforcement system The inspection records are reviewed to determine whether the target vehicle has red light running behavior.
The intelligent review system for off-site law enforcement pictures of vehicles running red lights according to claim 1, wherein the information about illegal red light running includes the original synthetic pictures and vehicle number plate number data;

The interactive interface is set in terminals such as computers, mobile phones, and IPADs;

The selection and labeling tools include rectangular frame labeling, polygonal frame labeling and multi-line segment labeling.
The intelligent review system for off-site law enforcement pictures of vehicles running red lights according to claim 1, wherein the attribute information includes attribute information of a static target and attribute information of a dynamic target, wherein the static target includes a stop at an entrance and exit. Line, left-turn boundary line, right-turn boundary line, and signal lights. The attribute information of the entrance and exit lane stop line, left-turn boundary line, and right-turn boundary line are polyline types and position information, and the attribute information of the signal lights is Rectangular frame type and its lamp head position information. The rectangular frame type includes left turn arrow light, straight arrow light, right turn arrow light, left turn round pie light, straight round pie light, straight left round pie light, U-turn arrow light, Special lights for public transportation; the dynamic target is the vehicle in the original composite picture, and the vehicle outline is marked by the selection marking tool, and vehicle attribute data is output.
The intelligent review system for off-site law enforcement pictures of vehicles running red lights according to claim 3, characterized in that the picture preprocessing module further comprises: based on the attribute information of the static target, performing the said P1, P2 and P3 Positioning of static targets.
The intelligent review system for off-site law enforcement pictures of vehicles running red lights according to claim 4, wherein, in the deep learning module,

Obtain a vehicle recognition model based on the vehicle attribute data and deep learning algorithm training; and

The color and phase recognition model of the signal lamp is obtained through training based on the attribute information of the signal lamp.
An intelligent review method for off-site law enforcement pictures of vehicles running red lights, applied to the system of any one of claims 1 to 5, characterized in that the method adopts a deep learning method to identify dynamic targets in pictures, and combines target tracking algorithms Realize the positioning and trajectory reconstruction of the target vehicle, and realize the intelligent review of red light running behavior. The specific steps include:

S1. The access module is connected to the off-site law enforcement system to collect information on the violation of vehicle red light running, and split the original synthesized picture according to GA/T496-2014 requirements to obtain P1, P2, and P3;

S2. The deep learning module respectively identifies the location of the target vehicle whose red light running behavior is to be reviewed from P1, P2, and P3, and marks the contour of the vehicle;

S3. Determine the centroid coordinates of the contour positioning marks in P1, P2 and P3 respectively, and connect the three centroids in P1 according to the order of the pictures to generate the driving trajectory of the target vehicle;

S4. Mark the stop line and the boundary line position of the target vehicle in P1 based on the position information of the stop line, left turn boundary, and right turn boundary of the entrance lane;

S5. Detect whether there is an intersection between the travel trajectory and the stop line of the entrance road, if so, go to step S6; otherwise, detect whether the center of mass coordinates of P1 pass the stop line of the entrance road in the forward direction of the target vehicle; if so; , Then go to step S6; if not, determine that the target vehicle does not violate the law by running a red light, and end the process;

S6: Determine and obtain the turning behavior of the target vehicle at the entrance lane according to the positional relationship between the driving track and the position information of the left turn boundary line and the right turn boundary line;

S7. Based on the lamp head position information, use a deep learning algorithm to identify the signal lights and phase information of the target vehicle in P1 and P3. If the phases are the same, go to step S8; otherwise, the validity of the red light violation record corresponding to the target vehicle cannot be determined Sex, and end the process;

S8. Based on the determination result of the steering behavior and the recognition result of the signal light and its phase information, if the determination result is consistent with the recognition result, it is determined that the target vehicle does not have red light running behavior; otherwise, it is determined that the target vehicle is The illegal record of running a red light is valid.
The intelligent review method for off-site law enforcement pictures of vehicles running red lights according to claim 6, wherein step S2 comprises:

S21. For P1, use a deep learning algorithm to detect all vehicles in P1 and perform contour mark positioning on the image area where the vehicles are located; extract vehicle number plate number data from the illegal information, and determine the number of vehicles in P1 according to the license plate recognition algorithm Mark the location of the corresponding target vehicle number plate; combine the vehicle area and the position of the target vehicle number plate to determine the target vehicle position in P1, and realize the location marking of the initial position contour of the illegal vehicle to be reviewed;

S22. Use deep learning algorithms for P2 and P3 to detect all vehicles in the picture, and perform contour mark positioning on the image area where the vehicle is located; determine the initial position of the target vehicle based on the positioning mark of the initial position contour; use the target tracking algorithm CSRT, The final positioning of the target vehicle is obtained in P2 and P3; combined with the vehicle area and target tracking results, the position of the target vehicle in P2 and P3 is determined, and its contour is marked for positioning.
The intelligent review method for off-site law enforcement pictures of vehicles running red lights according to claim 6, wherein the steering behavior includes turning left, going straight, and turning right.
The intelligent review method for off-site law enforcement pictures of vehicles running red lights according to claim 8, characterized in that, in step S6: if there is an intersection between the driving track and the left turn boundary, it is determined that the target vehicle turns left; If there is an intersection between the driving track and the right turn boundary line, it is determined that the target vehicle turns right; if the driving track does not have an intersection point with the right turn boundary line and the left turn boundary line, then it is determined that the target vehicle goes straight.