Single-point bidirectional linkage electric bicycle violation snapshot system and method

Abstract

The invention discloses a single-point two-way linkage electric bicycle violation snapshot system and method, belongs to the technical field of traffic management, and is characterized in that a double-camera linkage system is combined with a video and trigger mode to comprehensively detect violation behaviors of an electric bicycle. The front camera captures details of riders, and the back camera performs relay tracking and accurately captures key information such as license plates and objects. Targets are intelligently tracked, vehicle and personnel characteristics are updated in real time, and the targets are ensured not to be lost. The system is high in intelligent degree, automatically triggers snapshot, saves storage resources, and accurately records violation evidences at the same time. The information of the front camera and the back camera is efficiently integrated, the violation condition is completely presented, the processing accuracy is improved, and the identity of a violation person is confirmed through fusion of various data such as face recognition, license plate character recognition and license plate RFID reading. According to the invention, the traffic management efficiency is effectively improved, the comprehensiveness and accuracy of peccancy detection are ensured, reliable data support is provided for the traffic management department, and the traffic order and safety can be maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management, and more specifically to a single-point two-way linkage electric bicycle violation capture system and method. Background Art

[0002] With the acceleration of urbanization, electric bicycles, as a convenient and environmentally friendly means of transportation, play an increasingly important role in urban transportation. However, the number of electric bicycle violations has also increased, such as running red lights, driving in the wrong direction, occupying motor vehicle lanes, not wearing safety helmets, riding with passengers, illegal overloading, etc. These violations not only disrupt traffic order, but also pose a serious threat to traffic safety. Therefore, it is particularly important to develop an efficient and accurate electric bicycle violation capture system.

[0003] Traditional traffic violation capture systems mostly rely on manual management or single monitoring equipment, which has many limitations. For example, manual management has many disadvantages. On the one hand, manual law enforcement is inefficient and difficult to cover many traffic intersections and sections, and there are many blind spots in supervision; on the other hand, it consumes a lot of manpower and material resources, and the law enforcement effect is poor.

[0004] Single monitoring equipment such as CN114125395A riding electric bicycle violation monitoring device and monitoring method, CN118135805A intelligent traffic monitoring method and system based on the Internet of Things, CN110826508A a pedestrian non-motor vehicle violation detection and tracking method based on video stream, etc. The advantage of this method is that the detection is convenient, and the capture of some electric bicycle violations can be achieved through the deployment of a single camera; the disadvantage is that since a single camera can only capture information from a certain angle, it is impossible to take into account both the front face and the back license plate at the same time. When detecting the back, it is impossible to realize some situations where there is no license plate or the license plate is blocked and the face needs to be compared; when detecting the front, the license plate information cannot be recognized.

[0005] Existing multi-camera linkage capture all uses multi-camera linkage to capture at multiple points and multiple perspectives, and extracts the features of the target at various angles through cameras with different perspectives, such as CN114449144A multi-channel multi-point camera capture linkage device and method, CN110738150A camera linkage capture method, device and computer storage medium, etc. The advantage of this type of method is that by deploying multiple cameras at different locations, it is possible to capture the target at all angles; the disadvantage is that the deployment locations are relatively scattered, and coordinate system mapping is required, which has high requirements for target alignment in multiple cameras. Some also require the installation of specific markers in the monitoring scene to achieve multi-camera coordinate system mapping, so as to achieve accurate matching of the targets; CN201910936659.0, this patented camera linkage capture method uses two separate shooting systems to capture at least two traffic images of the same monitoring area, where the two shooting systems have different shooting angles, and maps the two traffic images to the same coordinate system to match the same target in the two traffic images. According to at least two traffic images in the same coordinate system, the target is identified, which can achieve matching of vehicle information and personnel information in the traffic images.

[0006] The installation and networking costs of multiple cameras and multiple locations are extremely high.

[0007] Therefore, how to provide a system and method for accurately capturing electric bicycle violations at a single point and significantly reduce installation and network costs is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0008] In view of this, the present invention provides a single-point two-way linkage electric bicycle violation capture system and method, which realizes comprehensive and accurate identification of various violations of electric bicycles and rapid acquisition and automatic notification of violators' information by constructing a two-way linkage high-definition image acquisition and intelligent analysis mechanism at the same point, thereby significantly improving traffic law enforcement efficiency, reducing management costs, and facilitating the construction of smart transportation.

[0009] In order to achieve the above object, the present invention adopts the following technical solution:

[0010] A single-point two-way linkage electric bicycle violation capture system, comprising: a collection module, a violation analysis module, and an identity recognition module;

[0011] The acquisition module includes a first camera, a second camera, an analysis unit, and a control unit. The first camera is used to acquire monitoring data of the monitoring area in real time, and to capture frontal photos of people and vehicles according to the control instructions of the control unit; the second camera is used to acquire backside photos of people and vehicles; the analysis unit is used to analyze according to the monitoring data and generate analysis results; the control unit is used to generate control instructions according to the analysis results of the analysis unit to control the first camera and the second camera to capture;

[0012] The violation analysis module is used to analyze whether the electric bicycle violates the law based on the front photo of the person and the vehicle and the back photo of the person and the vehicle;

[0013] The identity recognition module includes an RFID reading unit, a judgment unit, an information extraction unit, and a notification unit. The RFID reading unit reads the license plate information of the license plate containing the RFID chip through a wireless antenna; the judgment unit is used to judge whether the license plate information is read successfully; the information extraction unit is used to obtain the violator information based on the license plate information if the license plate information is read successfully; if the license plate information reading fails, the violator information is obtained based on the frontal photo of the person and the vehicle; the notification unit is used to send the violation information to the corresponding violator based on the violator information.

[0014] Preferably, the violation analysis module uses image analysis software based on a deep learning algorithm to perform deep neural network processing and analysis on the front and back photos of the person and vehicle to identify violations of the electric bicycle.

[0015] Preferably, the information extraction unit includes: a query unit, a face recognition unit, and a matching unit;

[0016] The query unit is used to query the vehicle registration information database according to the license plate information to obtain the registered person information corresponding to the license plate information, that is, the violator information;

[0017] The face recognition unit is used to perform face recognition on the frontal photo of the person and vehicle to obtain the facial information of the violator;

[0018] The matching unit is used to compare and match the facial information with the face database to obtain the information of the violator.

[0019] Preferably, the analysis unit is configured as follows:

[0020] At the same point, the monitoring data of the first camera is received in real time, and whether there is an electric bicycle is detected based on the monitoring data. When an electric bicycle is detected, the analysis result is sent to the control unit, and the vehicle characteristics are continued to be obtained based on the frontal photo of the person and the vehicle returned by the first camera. The electric bicycle is tracked in the monitoring data based on the vehicle characteristics. After the electric bicycle disappears from the monitoring range of the first camera, a relay instruction is sent to the second camera through the control unit to control the second camera to obtain monitoring data, and the capture time of the second camera is determined based on the monitoring data.

[0021] On the other hand, the present invention also provides a single-point two-way linkage electric bicycle violation capture method, comprising:

[0022] Acquire monitoring data of the monitoring area in real time, and detect the electric bicycle according to the monitoring data, and when the electric bicycle is detected, start the first camera to obtain a frontal photo of the rider;

[0023] Controlling a second camera to obtain a photo of the back of the cyclist based on the monitoring data;

[0024] Analyze whether the electric bicycle violates traffic rules based on the front photo of the person and the bicycle and the back photo of the person and the bicycle;

[0025] If the electric bicycle violates traffic rules, the license plate information is read based on the back photo of the person and the bicycle;

[0026] Determine whether the license plate information is read successfully, if successful, obtain the violator's information based on the license plate information; if unsuccessful, obtain the violator's information based on the frontal photo of the person and the vehicle;

[0027] Based on the information of the traffic violator, the traffic violation information is sent to the corresponding traffic violator.

[0028] Preferably, analyzing whether the electric bicycle is in violation of traffic rules based on the front photo of the person and the bicycle and the back photo of the person and the bicycle comprises:

[0029] Image analysis software based on deep learning algorithms is used to deeply process and analyze the front and back photos of the person and vehicle to identify violations of electric bicycles.

[0030] Preferably, monitoring data of the monitoring area is acquired in real time, and electric bicycle detection is performed according to the monitoring data. When an electric bicycle is detected, a first camera is activated to acquire a frontal photo of the rider and the electric bicycle, and a second camera is controlled based on the monitoring data to acquire a backside photo of the rider and the electric bicycle, including:

[0031] Receive the monitoring data of the first camera in real time, and detect whether there is an electric bicycle based on the monitoring data; when an electric bicycle is detected, send the analysis result to the control unit, and continue to obtain vehicle features based on the frontal photo of the person and the vehicle returned by the first camera, track the electric bicycle in the monitoring data based on the vehicle features, and after the electric bicycle disappears from the monitoring range of the first camera, send a relay instruction to the second camera through the control unit, control the second camera to obtain monitoring data, and determine the capture time of the second camera based on the monitoring data.

[0032] Preferably, image analysis software based on a deep learning algorithm is used to control the relay linkage capture of the first camera and the second camera at the same point.

[0033] It can be seen from the above technical solutions that compared with the prior art, the present invention discloses a single-point two-way linkage electric bicycle violation capture system and method, which realizes single-point two-way linkage violation capture by installing two cameras on the same pole position and controlling the two cameras in a coordinated manner. The dual-camera linkage system combines video and trigger mode to comprehensively detect electric bicycle violations. The front camera captures the details of the rider, such as helmet wearing, violations, etc.; the rear camera relays and tracks, accurately capturing key information such as license plates and cargo. Intelligently track the target, update vehicle and personnel characteristics in real time, and ensure that the target is not lost. The system has a high degree of intelligence, automatically triggers capture, saves storage resources, and accurately records evidence of violations. The information of the front and rear cameras is efficiently integrated to fully present the violation situation and improve processing accuracy. In addition, RFID chip data integration technology provides additional accurate information to make up for the lack of license plate recognition and reduce management confusion. The present invention effectively improves the efficiency of traffic management, ensures the comprehensiveness and accuracy of violation detection, provides reliable data support for traffic management departments, and helps maintain traffic order and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0035] Figure 1 It is the overall structural block diagram of the system of the present invention.

[0036] Figure 2 This is a structural diagram for actual application.

[0037] Figure 3 To capture example pictures.

[0038] Figure 4 This is a schematic diagram of the front view of a person and a bicycle captured on an electric bicycle riding in the wrong direction.

[0039] Figure 5 Schematic diagram of the photo of the rear license plate of an electric bicycle rider riding in the wrong direction.

[0040] Figure 6 A flowchart for obtaining violation information, receiving SMS reminders, and receiving penalties through public accounts. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The embodiment of the present invention discloses a single-point two-way linkage electric bicycle violation capture system, such as Figure 1-3 As shown, it includes: collection module, violation analysis module, and identity recognition module;

[0043] The acquisition module includes a first camera, a second camera, an analysis unit, and a control unit. The first camera is used to obtain monitoring data of the monitoring area in real time, and to capture front photos of people and vehicles according to the control instructions of the control unit; the second camera is used to obtain back photos of people and vehicles; the analysis unit is used to analyze according to the monitoring data and generate analysis results; the control unit is used to generate control instructions according to the analysis results of the analysis unit to control the first camera and the second camera to capture;

[0044] The violation analysis module is used to analyze whether the electric bicycle violates the law based on the front and back photos of the person and the vehicle; it combines deep learning algorithms and image analysis technology to comprehensively identify the violations of electric bicycles. For different violations, corresponding analysis modules are used for processing. For example, the helmet detection model is used to determine whether the rider is wearing a helmet, the passenger detection algorithm is used to analyze whether there is illegal passenger, and the license plate recognition and analysis module is used to process the license plate information. The identified violation results are associated and integrated with the vehicle information and the rider information to generate complete violation record data.

[0045] The identity recognition module includes a reading unit, a judgment unit, an information extraction unit, and a notification unit. The reading unit is used to read the license plate information, wherein the reading unit includes RFID reading and license plate character recognition. If the license plate has been updated to have an RFID chip, the RFID reads the license plate information data, and merges the data with the pictures captured by the forward and reverse cameras and transmits it back to the platform. If the license plate is an ordinary license plate, the license plate character recognition is performed to obtain the license plate information; the judgment unit is used to determine whether the license plate information is read successfully; the information extraction unit is used to obtain the violator information based on the license plate information if the license plate information is read successfully; if the license plate information fails to be read, the violator information is obtained based on the front photo of the person and the vehicle; the notification unit is used to send the violation information to the corresponding violator based on the violator information.

[0046] Automatically generate notification SMS content based on the information of the violating vehicle and cyclist, including detailed information such as the time, location, type of violation, fine amount, payment method, and rectification requirements. Through the cooperation interface with SMS operators, the notification SMS will be sent to the mobile phone number of the violator in a timely manner. At the same time, the system records the SMS sending status and time for subsequent query and statistical analysis. Figure 6 A flowchart for obtaining violation information, receiving SMS reminders, and receiving penalties through public accounts.

[0047] The first camera uses high resolution (no less than 8 megapixels) and advanced optical imaging technology to clearly capture the front features of the rider on the electric bike, ensuring that the facial image can be used for subsequent facial recognition and identity recognition analysis. Figure 3 Figure 5 is a frontal photo of the cyclist. It has real-time video monitoring and image capture functions. When the electric bicycle is running normally or suspected of violating traffic rules, it will upload image data to the edge AI server in time. When a vehicle is detected to be driving in the opposite direction, a linkage mechanism is established with the reverse camera. According to the preset linkage protocol, the reverse camera is triggered to capture the rear license plate of the electric bicycle in the opposite direction after a suitable delay time, providing key information for accurately identifying the identity of the illegal vehicle.

[0048] The second camera also has a high-resolution imaging capability of 8 megapixels or more. It faces the rear of the electric bike and is mainly responsible for capturing the rear license plate of the electric bike and recording the wrong-way behavior. Figure 3 6 in the middle is the photo of the rear license plate. The strategy of relay tracking and matching and delayed capture is adopted. The delay time is calculated based on the speed of the electric bicycle obtained by the forward camera through target tracking in video mode, which is generally 3-6 seconds, to ensure that the vehicle is captured when it completely enters the shooting range and the license plate is clearly visible. When capturing the wrong-way vehicle and face, the forward camera relay tracking and matching and delayed capture can be linked to work together to capture the rear license plate of the wrong-way vehicle and other violations, so as to achieve the complementarity and improvement of two-way information.

[0049] The analysis unit, control unit, and violation analysis module are implemented through the edge server. The edge service station establishes a stable and reliable connection between two high-definition smart cameras through a high-speed Ethernet cable or a dedicated industrial network, and receives real-time video stream data and captured image data uploaded by the camera. An efficient data transmission protocol and data caching mechanism are used to ensure the real-time and integrity of data transmission, and avoid the impact of data loss or delay on the accuracy of violation detection. Figure 2 As shown, it is an installation diagram for actual application, including a forward camera 1, a reverse camera 2, an edge server 3, and an RFID reading antenna 4.

[0050] like Figure 4-5 During the monitoring process, the reverse camera independently analyzes the vehicle's driving trajectory. The vehicle's driving path is obtained by tracking and calculating the changes in the vehicle's position in continuous video frames. The AI ​​algorithm is used to fit and analyze the vehicle's driving path to determine whether it is opposite to the preset normal driving direction. If it is determined to be reverse driving, relevant pictures are captured immediately, and through the linkage of the forward camera, photos of the rear of the vehicle and the surrounding environment are captured. If it is a license plate with an RFID chip, the information is read, and the analysis results of the reverse behavior, the captured pictures and the vehicle information are uploaded to the platform through the edge server. After the platform receives the reverse-related data, it will be included in the overall violation record for management and processing, generate a reverse violation notification, and perform subsequent operations according to the above data processing and notification process.

[0051] Preferably, the violation analysis module uses image analysis software based on deep learning algorithms to deeply process and analyze the front and back photos of people and vehicles to identify violations of electric bicycles. Among them, violations include but are not limited to: whether the rider follows traffic signals; whether the rider wears a helmet; whether the rider uses handheld communication tools, browses electronic devices, smokes, eats, etc., which hinders safe driving; whether there is illegal carrying of passengers; whether the electric vehicle is equipped with a rainproof roof; whether the electric vehicle hangs a license plate; whether the license plate is blocked or contaminated; towing animals or dragging people and cargo devices; whether driving in a non-motorized vehicle lane; driving on the right side of the carriageway on a road without a non-motorized vehicle lane; and other violations.

[0052] Furthermore, when the vehicle does not have a license plate or the license plate information is abnormal (including but not limited to: license plate pollution, obstruction, non-local license plate or fake license plate without RFID chip, etc.), the vehicle information cannot be obtained through RFID, so face matching is required to identify the violator. Therefore, the information extraction unit includes: query unit, face recognition unit, matching unit;

[0053] The query unit is used to query the vehicle registration information database according to the license plate information to obtain the registered person information corresponding to the license plate information, that is, the information of the violator; query the vehicle registration information database of the traffic police system to obtain the registered person information of the vehicle, including the owner's name, ID number, contact information, vehicle registration address and other detailed information. Establish a data security interaction interface with the traffic police system to ensure the legality, security and efficiency of the query process.

[0054] The face recognition unit is used to perform face recognition on the frontal photos of people and vehicles to obtain the facial information of the violators;

[0055] The matching unit is used to compare and match facial information with the face database to obtain information about traffic violators. The face database integrates the public security population information database or other legally obtained identity information resources, and uses a multi-dimensional face feature comparison algorithm to quickly and accurately obtain the identity information and mobile phone number of the violator.

[0056] Preferably, the analysis unit is configured as follows:

[0057] Receive the monitoring data of the first camera in real time, and detect whether there is an electric bicycle based on the monitoring data. When an electric bicycle is detected, send the analysis result to the control unit, and continue to obtain the vehicle characteristics based on the front photo of the person and the vehicle returned by the first camera, track the electric bicycle in the monitoring data based on the vehicle characteristics, and after the electric bicycle disappears from the monitoring range of the first camera, send a relay instruction to the second camera through the control unit, control the second camera to obtain monitoring data, and determine the capture time of the second camera based on the monitoring data.

[0058] On the other hand, the present invention also provides a single-point two-way linkage electric bicycle violation capture method, comprising:

[0059] Acquire monitoring data of the monitoring area in real time, and detect the electric bicycle according to the monitoring data. When the electric bicycle is detected, start the first camera to obtain the frontal photo of the rider;

[0060] Controlling the second camera based on the monitoring data to obtain a photo of the back of the cyclist;

[0061] Analyze whether the electric bicycle violates traffic rules based on the front and back photos of the person and the bicycle;

[0062] If an electric bicycle violates traffic rules, the license plate information is read based on the photo of the back of the person or vehicle;

[0063] Determine whether the license plate information is read successfully. If successful, obtain the violator's information based on the license plate information; if unsuccessful, obtain the violator's information based on the frontal photo of the person and the vehicle;

[0064] Based on the information of the violator, the violation information is sent to the corresponding violator.

[0065] Preferably, analyzing whether the electric bicycle is in violation of traffic rules based on the front photo of the person and the bicycle and the back photo of the person and the bicycle includes:

[0066] Image analysis software based on deep learning algorithms is used to deeply process and analyze front and back photos of people and vehicles to identify violations of electric bicycles.

[0067] Preferably, monitoring data of the monitoring area is acquired in real time, and electric bicycle detection is performed according to the monitoring data. When an electric bicycle is detected, a first camera is activated to acquire a frontal photo of the rider and the electric bicycle, and a second camera is controlled based on the monitoring data to acquire a backside photo of the rider and the electric bicycle, including:

[0068] Receive the monitoring data of the first camera in real time, and detect whether there is an electric bicycle based on the monitoring data. When an electric bicycle is detected, send the analysis result to the control unit, and continue to obtain the vehicle characteristics based on the front photo of the person and the vehicle returned by the first camera, track the electric bicycle in the monitoring data based on the vehicle characteristics, and after the electric bicycle disappears from the monitoring range of the first camera, send a relay instruction to the second camera through the control unit, control the second camera to obtain monitoring data, and determine the capture time of the second camera based on the monitoring data.

[0069] The video mode of the front camera is analyzed in real time, and once an electric bike is detected, the target tracking algorithm is immediately activated. The algorithm continuously tracks the position, size, shape and other features of the electric bike in the video frame, calculates the vehicle's real-time speed, direction of travel, and relative position change information, and at the same time, extracts and records the characteristics of the rider (such as clothing, facial features, etc.) and the vehicle's body features (such as color, style, etc.), establishes a target information file of the vehicle and rider, and continuously updates this information in video mode.

[0070] When the front camera video mode detects that the electric vehicle meets certain trigger conditions (such as entering a specific capture area, suspected violations, etc.), the edge AI server sends a trigger command to the front camera to start the trigger mode of the front camera. According to the trigger command, the front camera captures high-definition images of faces, helmets, smoking, mobile phone use, illegal passenger transportation, etc., and transmits the captured image data to the edge server.

[0071] When the electric vehicle disappears from the front camera's field of view and enters the rear camera's range, the rear camera starts relay tracking based on the updated vehicle body and rider feature information transmitted by the edge server. The vehicle and rider are tracked and monitored in the rear camera video mode. Once the license plate information is detected, the edge server sends a trigger command to the rear camera, triggering the rear camera to capture images of the license plate number, license plate occlusion, license plate damage, and other information, and transmit the merged data to the server for subsequent processing.

[0072] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0073] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A single-point two-way linkage electric bicycle violation capture system, characterized in that: include: Collection module, violation analysis module, identity recognition module; The acquisition module includes a first camera, a second camera, an analysis unit, and a control unit. The first camera is used to obtain monitoring data of the monitoring area in real time and to capture frontal photos of people and vehicles according to the control instructions of the control unit; the second camera is used to obtain backside photos of people and vehicles; The analysis unit is used to analyze the monitoring data and generate analysis results; The control unit is used to generate a control instruction according to the analysis result of the analysis unit to control the first camera and the second camera to capture; The violation analysis module is used to analyze whether the electric bicycle violates the law based on the front photo of the person and the vehicle and the back photo of the person and the vehicle; The identity recognition module includes a reading unit, a judgment unit, an information extraction unit, and a notification unit. The reading unit reads the license plate information; the judgment unit is used to judge whether the license plate information is read successfully; the information extraction unit is used to obtain the information of the violator based on the license plate information if the license plate information is read successfully; If the license plate information fails to be read, the violator's information is obtained based on the frontal photo of the person and the vehicle; the notification unit is used to send the violation information to the corresponding violator based on the violator's information.

2. A single-point two-way linkage electric bicycle violation capture system according to claim 1, characterized in that: The violation analysis module uses image analysis software based on a deep learning algorithm to perform deep neural network analysis on the front and back photos of the person and vehicle to identify violations of the electric bicycle.

3. The single-point two-way linkage electric bicycle violation capture system according to claim 1, characterized in that: The information extraction unit includes: a query unit, a face recognition unit, and a matching unit; The query unit is used to query the vehicle registration information database according to the license plate information to obtain the registered person information corresponding to the license plate information, that is, the violator information; The face recognition unit is used to perform face recognition on the frontal photo of the person and vehicle to obtain the facial information of the violator; The matching unit is used to compare and match the facial information with the face database to obtain the information of the violator.

4. The single-point two-way linkage electric bicycle violation capture system according to claim 1, characterized in that: The analysis unit is configured to: At the same point, the monitoring data of the first camera is received in real time, and whether there is an electric bicycle is detected based on the monitoring data. When an electric bicycle is detected, the analysis result is sent to the control unit, and the vehicle characteristics are continued to be obtained based on the frontal photo of the person and the vehicle returned by the first camera. The electric bicycle is tracked in the monitoring data based on the vehicle characteristics. After the electric bicycle disappears from the monitoring range of the first camera, a relay instruction is sent to the second camera through the control unit to control the second camera to obtain monitoring data, and the capture time of the second camera is determined based on the monitoring data.

5. A single-point two-way linkage electric bicycle violation capture method, characterized in that: include: Acquire monitoring data of the monitoring area in real time, and detect the electric bicycle according to the monitoring data, and when the electric bicycle is detected, start the first camera to obtain a frontal photo of the rider; Controlling a second camera to obtain a photo of the back of the cyclist based on the monitoring data; Analyze whether the electric bicycle violates traffic rules based on the front photo of the person and the bicycle and the back photo of the person and the bicycle; If the electric bicycle violates traffic rules, the license plate information is read based on the back photo of the person and the bicycle; Determine whether the license plate information is read successfully, and if so, obtain the violator information based on the license plate information; If it fails, the violator’s information is obtained based on the frontal photo of the person and the vehicle; Based on the information of the traffic violator, the traffic violation information is sent to the corresponding traffic violator.

6. A single-point two-way linkage electric bicycle violation capture method according to claim 5, characterized in that: Analyzing whether the electric bicycle violates traffic rules based on the front photo of the person and the bicycle and the back photo of the person and the bicycle includes: Image analysis software based on deep learning algorithms is used to deeply process and analyze the front and back photos of the person and vehicle to identify violations of electric bicycles.

7. A single-point two-way linkage electric bicycle violation capture method according to claim 5, characterized in that: Acquire monitoring data of a monitoring area in real time, and detect an electric bicycle according to the monitoring data; when an electric bicycle is detected, start a first camera to obtain a frontal photo of the rider and the bicycle, and control a second camera based on the monitoring data to obtain a backside photo of the rider and the bicycle, including: Receive the monitoring data of the first camera in real time, and detect whether there is an electric bicycle based on the monitoring data; when an electric bicycle is detected, send the analysis result to the control unit, and continue to obtain vehicle features based on the frontal photo of the person and the vehicle returned by the first camera, track the electric bicycle in the monitoring data based on the vehicle features, and after the electric bicycle disappears from the monitoring range of the first camera, send a relay instruction to the second camera through the control unit, control the second camera to obtain monitoring data, and determine the capture time of the second camera based on the monitoring data.

8. A single-point two-way linkage electric bicycle violation capture method according to claim 5, characterized in that: Image analysis software based on deep learning algorithms is used at the same point to control the relay linkage capture of the first camera and the second camera.

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