Vehicle illegal behavior monitoring method and system based on single Beidou video positioning

Abstract

The invention relates to the technical field of vehicle illegal behavior monitoring, and discloses a vehicle illegal behavior monitoring method and system based on single Beidou video positioning, and the method comprises the steps: obtaining Beidou positioning data generated by a vehicle through a single Beidou positioning system; when the position coordinates of the vehicle fall into the electronic fence range of the illegal monitoring area, calling a video stream collected by roadside video equipment covering the position coordinates of the vehicle; extracting a video frame sequence matched with the timestamp from the video stream and identifying behavior characteristics of the vehicle in the video frame sequence; and comparing the behavior characteristics with an illegal behavior rule base in real time, and if the comparison succeeds, generating an illegal evidence packet. The problems of high equipment cost of a multi-satellite positioning system and inaccurate positioning of a signal shielding area can be avoided, the data fusion complexity is reduced, the processing speed is improved, the real-time monitoring requirement is met, the problems of limited coverage range, high cost, poor real-time performance and the like of vehicle illegal behavior monitoring are effectively solved, and the efficiency and accuracy of traffic law enforcement are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle violation monitoring technology, and in particular to a vehicle violation monitoring method and system based on single Beidou video positioning. Background Art

[0002] In the field of vehicle violation monitoring, many existing methods have numerous problems. On the one hand, traditional monitoring methods, such as fixed camera capture, have limited coverage and are difficult to be effective in some complex road conditions or remote areas. On the other hand, some monitoring methods based on multi-satellite positioning systems have high equipment requirements, which also increases costs, and may cause inaccurate positioning in areas with signal obstruction. At the same time, some technologies that combine positioning and video monitoring have complex data fusion and slow processing speeds, making it difficult to meet the high requirements of real-time monitoring. As a result, it is impossible to capture vehicle violations in a timely and accurate manner, affecting the efficiency and accuracy of traffic law enforcement.

[0003] Therefore, it is urgent to provide a technical solution to solve the above problems. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a vehicle violation monitoring method and system based on single Beidou video positioning.

[0005] In a first aspect, the present invention provides a method for monitoring vehicle violations based on single Beidou video positioning. The technical solution of the method is as follows: Obtaining Beidou positioning data generated in real time by a single Beidou positioning system for the target vehicle, wherein the Beidou positioning data includes the vehicle position coordinates and a timestamp; When the vehicle position coordinates fall within the preset illegal monitoring area electronic fence, the video stream collected in real time by the roadside video equipment covering the vehicle position coordinates is retrieved; Extracting a target video frame sequence matching the timestamp from the video stream, and identifying target behavior features of the target vehicle in the target video frame sequence based on a computer vision algorithm; The target behavior characteristics are compared with the preset illegal behavior rule library in real time. If the comparison is successful, an illegal evidence package is generated. The illegal evidence package includes Beidou positioning data, target video frame sequence and main illegal type identification.

[0006] The beneficial effects of the vehicle violation monitoring method based on single Beidou video positioning of the present invention are as follows: The method of the present invention avoids the problems of high equipment requirements and high cost of multi-satellite positioning systems and inaccurate positioning in signal-blocked areas by utilizing a single Beidou positioning system. At the same time, it reduces the complexity of data fusion, improves processing speed, meets real-time monitoring requirements, and effectively solves the problems of limited coverage, high cost, and poor real-time performance of vehicle violation monitoring in the existing technology, thereby improving the efficiency and accuracy of traffic law enforcement.

[0007] Based on the above solution, the vehicle violation monitoring method based on single Beidou video positioning of the present invention can also be improved as follows.

[0008] In an optional manner, the method further includes: Load the illegal monitoring area electronic fence boundary coordinate set pre-stored in the database; Calculate the spatial position relationship between the vehicle position coordinates and the electronic fence boundary coordinate set of the illegal monitoring area; Based on the calculation result, it is determined whether the vehicle position coordinates are within the electronic fence of the illegal monitoring area surrounded by the set of boundary coordinates of the electronic fence of the illegal monitoring area.

[0009] In an optional manner, obtaining Beidou positioning data generated in real time by a single Beidou positioning system for a target vehicle further includes: Receiving a Beidou positioning message sent by a vehicle-mounted Beidou terminal corresponding to the single Beidou positioning system; wherein the vehicle-mounted Beidou terminal generates the Beidou positioning message according to the received satellite signal of the single Beidou positioning system; Performing data parsing on the Beidou positioning message to extract the vehicle position coordinates and timestamp; Encapsulating the extracted vehicle position coordinates and the timestamp into structured Beidou positioning data; The Beidou positioning message is transmitted in real time through a wireless communication network, and the timestamp is derived from a single Beidou positioning system clock source.

[0010] In an optional manner, the retrieving a video stream captured in real time by a roadside video device covering the vehicle position coordinates further includes: Matching a target roadside video device identifier covering the vehicle position coordinates from a video device geographic information database; A video stream retrieving instruction is sent to the corresponding roadside video device according to the target roadside video device identifier to receive the video stream returned in real time by the roadside video device.

[0011] In an optional manner, the identifying target behavior features of the target vehicle in the target video frame sequence based on a computer vision algorithm further includes: Based on the target detection algorithm, dynamically locate the image area of ​​the target vehicle in each frame image of the target video frame sequence; Extracting the spatiotemporal motion feature vector of the image region in each frame of image by motion analysis algorithm; All spatiotemporal motion feature vector sequences are converted into target behavior features through the behavior feature mapping function.

[0012] In an optional manner, the expression of the behavior feature mapping function is:

[0013] Where, Indicates the target behavior characteristics, Represents the spatiotemporal motion feature vector of consecutive T frame images, Indicates the total number of frames in the target video frame sequence, represents the spatiotemporal motion feature vector of the t-th frame image, Indicates the number of preset illegal behavior feature dimensions, represents the dynamic weight parameter of the k-th dimension illegal behavior feature of the t-th frame, The spatial transformation matrix representing the k-th dimension of the illegal behavior feature, represents the bias vector of the k-th dimension illegal behavior feature, represents a nonlinear activation function; in, ; , represents the rate of change of the horizontal displacement of the center of mass of the target vehicle between consecutive frames; represents the horizontal coordinate of the center of mass pixel of the target vehicle in the tth frame, Represents the time interval from frame t to frame t+1; , represents the rate of change of vertical displacement of the center of mass of the target vehicle between consecutive frames; Indicates the pixel ordinate of the center of mass of the target vehicle in frame t; Indicates the offset of the vehicle direction angle between consecutive frames; represents the direction angle of the target vehicle in frame t; , represents the contour deformation coefficient of the target vehicle, is the contour point set, represents the contour point set of the target vehicle in the tth frame, is the base area; , represents the position entropy of the target vehicle, based on to Period calculation; Indicates the number of lane segmentation areas, Indicates the A segmented area, Indicates that the target vehicle is in The probability of occurrence of a segmented region.

[0014] In an optional manner, the target behavior characteristics are compared with a preset illegal behavior rule library in real time, and if the comparison is successful, an illegal evidence package is generated, further comprising: Obtain target behavior characteristics The illegal behavior rule library Characteristic template vector of class illegal type Matching degree ,in , is the total number of violation types; Determine satisfaction Illegal type identification set ; Based on the preset violation type priority rules, from the violation type identification set Identify the main violation type in ; Generate an illegal evidence package, which includes the Beidou positioning data, the target video frame sequence and the main illegal type identifier ; Among them, the main violation type identification The formula for determining is:

[0015] Where, Represents a set of illegal type identifiers, Indicates the number of violation types in the violation type identifier set, Represents the matching weight coefficient, represents the weight coefficient of the severity of the violation, Indicates the Preset severity scores for the offense type; Among them, the matching degree The calculation formula is:

[0016] Where, Indicates the The feature template vector of the class violation type, Indicates the Type of illegal Characteristics of illegal behavior, The first Characteristics of illegal behavior, Indicates the Rule weight parameters that measure illegal behavior characteristics , represents the eigenvalue difference threshold indicator, Indicates the The standard deviation tolerance parameter of the illegal behavior characteristics, represents the overall similarity enhancement coefficient, Indicates the The minimum match threshold for the violation type.

[0017] In a second aspect, the present invention provides a vehicle violation monitoring system based on single Beidou video positioning, the technical solution of the system is as follows: An acquisition module is used to acquire Beidou positioning data generated in real time by a single Beidou positioning system for a target vehicle, wherein the Beidou positioning data includes vehicle position coordinates and a timestamp; A retrieval module is used to retrieve the video stream collected in real time by the roadside video equipment covering the vehicle position coordinates when the vehicle position coordinates fall within the preset illegal monitoring area electronic fence range; an identification module, configured to extract a target video frame sequence matching the timestamp from the video stream, and identify target behavior features of the target vehicle in the target video frame sequence based on a computer vision algorithm; The monitoring module is used to compare the target behavior characteristics with the preset illegal behavior rule library in real time. If the comparison is successful, a violation evidence package is generated. The violation evidence package includes the Beidou positioning data, the target video frame sequence and the main violation type identifier.

[0018] The beneficial effects of the vehicle violation monitoring system based on single Beidou video positioning of the present invention are as follows: The system of the present invention avoids the problems of high equipment requirements and high cost of multi-satellite positioning systems and inaccurate positioning in signal-blocked areas by utilizing a single Beidou positioning system. At the same time, it reduces the complexity of data fusion, improves processing speed, meets real-time monitoring requirements, and effectively solves the problems of limited coverage, high cost, and poor real-time performance of vehicle violation monitoring in existing technologies, thereby improving the efficiency and accuracy of traffic law enforcement.

[0019] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0021] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the drawings. In the drawings: Figure 1 Schematic diagram of a flow chart of an embodiment of a vehicle violation monitoring method based on single Beidou video positioning of the present invention; Figure 2 This is a structural diagram of an embodiment of a vehicle violation monitoring system based on single Beidou video positioning of the present invention. DETAILED DESCRIPTION

[0022] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0023] Figure 1 The flowchart of an embodiment of a vehicle violation monitoring method based on single Beidou video positioning provided by the present invention is shown and executed by a control terminal. Figure 1 As shown, the following steps are included: S1. Obtain Beidou positioning data generated in real time by a single Beidou positioning system for a target vehicle, wherein the Beidou positioning data includes vehicle position coordinates and a timestamp.

[0024] Target vehicles are motor vehicles tracked in real time by a single Beidou positioning system, whose behavioral characteristics are analyzed by the monitoring system. Vehicle identity is determined by the unique identifier of the onboard Beidou terminal, and includes, but is not limited to, civilian cars, freight vehicles, and public transportation vehicles. A single Beidou positioning system refers to a satellite signal reception and processing system that relies solely on the Beidou satellite navigation system for positioning services. This system eliminates other satellite signal sources such as GPS and GLONASS, achieving positioning solutions using navigation signals transmitted by Beidou satellites. It consists of a space segment (Beidou satellite constellation), a ground control segment, and a user segment (onboard terminal). Beidou positioning data refers to the real-time data set generated by the single Beidou positioning system, consisting of three-dimensional vehicle position coordinates (longitude, latitude, and elevation) and corresponding timestamps. The data format complies with the Beidou civilian protocol standard and is transmitted via wireless communication networks. The timestamps are synchronized with the Beidou system clock source, with a time synchronization error of no more than 100 nanoseconds. Vehicle position coordinates refer to the real-time spatial position parameters of the target vehicle in the Earth coordinate system, expressed as a triplet of longitude, latitude, and elevation. The coordinate system uses the WGS-84 international standard, with positioning accuracy better than 1.5 meters (95% confidence level). This is obtained by parsing Beidou positioning messages. The timestamp is the precise time stamp used to record the target vehicle's position coordinates within Beidou positioning data. The time base is Beidou Atomic Time (BDT), with a time resolution of microseconds.

[0025] S2. When the vehicle position coordinates fall within the preset electronic fence range of the illegal monitoring area, a video stream collected in real time by a roadside video device covering the vehicle position coordinates is retrieved.

[0026] The electronic fence of the violation monitoring area refers to a closed polygonal geographic boundary area preset in a digital map. This area is defined by a sequence of longitude / latitude coordinate points and covers sections of road with high traffic violation rates (such as intersections and school areas). The monitoring mechanism is triggered when the vehicle's position coordinates enter this closed area. Roadside video equipment refers to video capture devices that are fixedly deployed on road infrastructure. The equipment has real-time video streaming capabilities, with a coverage radius of ≥50 meters, a frame rate of ≥25fps, and a resolution of ≥1080P. It is connected to the central controller via an IP network.

[0027] S3. Extracting a target video frame sequence that matches the timestamp from the video stream, and identifying target behavior features of the target vehicle in the target video frame sequence based on a computer vision algorithm.

[0028] Among them, the target video frame sequence refers to a set of related image frames extracted from the continuous video stream collected by the roadside video equipment according to the Beidou positioning data timestamp. The sequence time span covers the complete behavior process of the target vehicle within the electronic fence, and the time interval between frames is ≤40 milliseconds. The computer vision algorithm refers to a software method for analyzing video frames through image processing and pattern recognition technology. In this embodiment, it includes but is not limited to: ① target detection algorithm: locating the vehicle image area; ② motion analysis algorithm: extracting dynamic features such as vehicle displacement and deformation. The target behavior feature refers to a set of parameters that quantify the motion state of the target vehicle. It contains five-dimensional feature vectors: horizontal displacement change rate, vertical displacement change rate, angular offset, contour deformation coefficient and position entropy, which are generated by converting the spatiotemporal motion feature vector through a mapping function.

[0029] S4. Compare the target behavior characteristics with a preset illegal behavior rule library in real time. If the comparison is successful, generate an illegal evidence package, which includes the Beidou positioning data, the target video frame sequence and the main illegal type identifier.

[0030] The violation rule library refers to a collection of violation feature templates pre-stored in the database. Each record contains a violation type identifier, a feature template vector, a matching threshold, and a severity score, covering eight traffic violation categories, including speeding, running a red light, and driving against traffic. The violation evidence package is a structured data package containing a complete chain of evidence for the violation, including Beidou positioning data, a target video frame sequence, and a primary violation type identifier. The primary violation type identifier is a numeric code that uniquely identifies the violation category. For example, the coding rules are: 1xxx: Dangerous driving (e.g., drunk driving: 1001); 2xxx: Passing violation (e.g., running a red light: 2003); 3xxx: Parking violation (e.g., illegal parking: 3005).

[0031] The technical solution of this embodiment avoids the problems of high equipment requirements and high costs of multi-satellite positioning systems and inaccurate positioning in signal-blocked areas by utilizing a single Beidou positioning system. At the same time, it reduces the complexity of data fusion, improves processing speed, meets real-time monitoring requirements, and effectively solves the problems of limited coverage, high cost, and poor real-time performance of vehicle violation monitoring in existing technologies, thereby improving the efficiency and accuracy of traffic law enforcement.

[0032] In an optional manner, S1 specifically includes: S11. Receive a Beidou positioning message sent by a vehicle-mounted Beidou terminal corresponding to the single Beidou positioning system; wherein the vehicle-mounted Beidou terminal generates the Beidou positioning message based on the received satellite signal of the single Beidou positioning system.

[0033] S12: performing data parsing processing on the Beidou positioning message to extract the vehicle position coordinates and the timestamp.

[0034] S13: Encapsulate the extracted vehicle position coordinates and the timestamp into the structured Beidou positioning data.

[0035] The Beidou positioning message is transmitted in real time through a wireless communication network, and the timestamp is derived from a single Beidou positioning system clock source.

[0036] In the above optional method, the synchronization and accuracy of time are further guaranteed, which provides a reliable time and space benchmark for subsequent vehicle behavior feature identification and violation judgment, further improving the accuracy and reliability of monitoring.

[0037] In an optional manner, the method further includes: Load the illegal monitoring area electronic fence boundary coordinate set pre-stored in the database.

[0038] The spatial position relationship between the vehicle position coordinates and the electronic fence boundary coordinate set of the illegal monitoring area is calculated.

[0039] Based on the calculation result, it is determined whether the vehicle position coordinates are within the electronic fence of the illegal monitoring area surrounded by the set of boundary coordinates of the electronic fence of the illegal monitoring area.

[0040] In the above optional method, ineffective monitoring of other areas is further effectively avoided, the pertinence and efficiency of monitoring are improved, the system can focus on monitoring traffic violations in areas with high incidence of violations, optimize resource allocation, and enhance the practicality and effectiveness of the system.

[0041] In an optional manner, the retrieving a video stream captured in real time by a roadside video device covering the vehicle position coordinates further includes: Matching a target roadside video device identifier covering the vehicle position coordinates from a video device geographic information database; A video stream retrieving instruction is sent to the corresponding roadside video device according to the target roadside video device identifier to receive the video stream returned in real time by the roadside video device.

[0042] In the above optional method, it is further ensured that the required video data can be retrieved quickly and accurately after the vehicle enters the illegal monitoring area, reducing the search time and range of the video equipment, improving the system's response speed and monitoring efficiency, and providing timely video basis for the subsequent extraction and analysis of vehicle behavior characteristics.

[0043] In an optional manner, the identifying target behavior features of the target vehicle in the target video frame sequence based on a computer vision algorithm further includes: Based on the target detection algorithm, dynamically locate the image area of ​​the target vehicle in each frame image of the target video frame sequence; Extracting the spatiotemporal motion feature vector of the image region in each frame of image by motion analysis algorithm; All spatiotemporal motion feature vector sequences are converted into target behavior features through the behavior feature mapping function.

[0044] In the above optional method, the vehicle's behavior in the video, such as displacement changes, direction deviation, etc., can be further accurately identified, providing accurate behavioral feature data for subsequent comparison with the violation rule library, improving the accuracy and reliability of violation identification, and enhancing the system's monitoring capabilities.

[0045] In an optional manner, the expression of the behavior feature mapping function is:

[0046] Where, represents the target behavior characteristics, represents the mapping function, Represents the spatiotemporal motion feature vector of consecutive T frame images, represents the total number of frames of the target video frame sequence, represents the spatiotemporal motion feature vector of the t-th frame image, Indicates the number of preset illegal behavior feature dimensions, The dynamic weight parameter representing the k-th dimension of the illegal behavior feature in the t-th frame (the value range is 0-1, and the default is 0.5). During the behavior feature mapping process, the feature dimension weight is dynamically assigned according to the degree of feature mutation. This parameter strengthens the feature contribution of key illegal moments. The spatial transformation matrix representing the k-th dimension of the illegal behavior feature, represents the bias vector of the k-th dimension illegal behavior feature, represents a nonlinear activation function; in, ; , represents the rate of change of the horizontal displacement of the center of mass of the target vehicle between consecutive frames; represents the horizontal coordinate of the center of mass pixel of the target vehicle in the tth frame, Represents the time interval from frame t to frame t+1; , represents the rate of change of vertical displacement of the center of mass of the target vehicle between consecutive frames; Indicates the pixel ordinate of the center of mass of the target vehicle in frame t; Indicates the offset of the vehicle direction angle between consecutive frames; represents the direction angle of the target vehicle in frame t; , represents the contour deformation coefficient of the target vehicle, is the contour point set, represents the contour point set of the target vehicle in the tth frame, is the reference area (take the initial contour area of ​​the vehicle); , represents the position entropy of the target vehicle, based on to Period calculation; Indicates the number of lane segmentation areas, Indicates the A segmented area, Indicates that the target vehicle is in The probability of occurrence of a segmented area (obtained from the frequency statistics of the vehicle's center of mass coordinates falling into the area).

[0047] In the above optional methods, the behavioral characteristics of the vehicle can be further described more comprehensively and accurately, the accuracy and adaptability of the comparison with the violation rule library are improved, the system can more effectively identify various types of traffic violations, and the accuracy and intelligence of monitoring are improved.

[0048] In an optional manner, the step of comparing the target behavior characteristics with a preset illegal behavior rule library in real time and generating an illegal behavior evidence package if the comparison is successful further includes: Obtain target behavior characteristics The illegal behavior rule library Characteristic template vector of class illegal type Matching degree ,in , is the total number of violation types; Determine satisfaction Illegal type identification set ; Based on the preset violation type priority rules, from the violation type identification set Identify the main violation type in ; Generate an illegal evidence package, which includes the Beidou positioning data, the target video frame sequence and the main illegal type identifier ; Among them, the main violation type identification The formula for determining is:

[0049] Where, Represents a set of illegal type identifiers, Indicates the number of violation types in the violation type identifier set, Represents the matching weight coefficient, represents the weight coefficient of the severity of the violation, Indicates the The preset severity score of the violation type is a quantitative score of the predefined danger level of the violation based on traffic regulations and social harm. ; Matching weight coefficient It refers to the relative importance weight of the algorithm matching score when determining the main violation type. This coefficient controls the contribution ratio of the similarity between the target behavior characteristics and the violation template in the final decision. , the default value is ; Weight coefficient of severity of violation It refers to the relative importance weight of the degree of social harm caused by the illegal behavior when determining the main violation type. This coefficient reflects the penalty priority of traffic regulations. , the default value is .

[0050] Among them, the matching degree The calculation formula is:

[0051] Where, Indicates the The feature template vector of the class violation type, Indicates the Type of illegal Characteristics of illegal behavior, The first Characteristics of illegal behavior, Indicates the The rule weight parameter that measures the characteristics of illegal behavior, , represents the eigenvalue difference threshold indicator; Indicates the The standard deviation tolerance parameter of the dimensional violation feature (the value range is 0-1, the default is 0.2), which is used to control the decay rate of the Gaussian function; Indicates the overall similarity enhancement coefficient (the value range is 0.2-0.5, the default is 0.3), which is used to improve the contribution of the overall similarity of the feature vector to the matching degree; Indicates the The minimum match threshold for the class violation type (range 0.6-0.9, default 0.7); Indicates the The standard deviation tolerance parameter of the illegal behavior characteristics, Represents the overall similarity enhancement coefficient, which is used to improve the contribution of the overall similarity of the feature vector to the matching degree.

[0052] Among the above optional methods, it is further possible to accurately identify illegal behaviors and determine the most suitable main violation type among multiple possible violation types, thereby reducing misjudgments and missed judgments, improving the accuracy and completeness of violation evidence, and providing strong support for traffic law enforcement.

[0053] Figure 2 FIG. 1 shows a schematic diagram of an embodiment of a vehicle violation monitoring system based on single Beidou video positioning provided by the present invention. Figure 2 As shown, the system includes: An acquisition module 210 is configured to acquire Beidou positioning data generated in real time by a single Beidou positioning system for a target vehicle, wherein the Beidou positioning data includes vehicle position coordinates and a timestamp; The retrieval module 220 is configured to retrieve a video stream captured in real time by a roadside video device covering the vehicle's location coordinates when the vehicle's location coordinates fall within a preset illegal monitoring area electronic fence; an identification module 230 for extracting a target video frame sequence matching the timestamp from the video stream, and identifying target behavior features of the target vehicle in the target video frame sequence based on a computer vision algorithm; The monitoring module 240 is used to compare the target behavior characteristics with the preset illegal behavior rule library in real time. If the comparison is successful, an illegal evidence package is generated. The illegal evidence package includes the Beidou positioning data, the target video frame sequence and the main illegal type identification.

[0054] In an optional manner, the system further includes: a judgment module; the judgment module is configured to: Load the illegal monitoring area electronic fence boundary coordinate set pre-stored in the database; Calculate the spatial position relationship between the vehicle position coordinates and the electronic fence boundary coordinate set of the illegal monitoring area; Based on the calculation result, it is determined whether the vehicle position coordinates are within the electronic fence of the illegal monitoring area surrounded by the set of boundary coordinates of the electronic fence of the illegal monitoring area.

[0055] In an optional manner, the acquisition module 210 is specifically configured to: Receiving a Beidou positioning message sent by a vehicle-mounted Beidou terminal corresponding to the single Beidou positioning system; wherein the vehicle-mounted Beidou terminal generates the Beidou positioning message according to the received satellite signal of the single Beidou positioning system; Performing data parsing on the Beidou positioning message to extract the vehicle position coordinates and the timestamp; Encapsulating the extracted vehicle position coordinates and the timestamp into structured Beidou positioning data; The Beidou positioning message is transmitted in real time through a wireless communication network, and the timestamp is derived from a single Beidou positioning system clock source.

[0056] The technical solution of this embodiment avoids the problems of high equipment requirements and high costs of multi-satellite positioning systems and inaccurate positioning in signal-blocked areas by utilizing a single Beidou positioning system. At the same time, it reduces the complexity of data fusion, improves processing speed, meets real-time monitoring requirements, and effectively solves the problems of limited coverage, high cost, and poor real-time performance of vehicle violation monitoring in existing technologies, thereby improving the efficiency and accuracy of traffic law enforcement.

[0057] Furthermore, the above embodiments provide systems that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. Furthermore, the systems and method embodiments provided in the above embodiments share the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0058] The above description is merely an illustration of preferred embodiments of the present invention and the underlying technical principles. Those skilled in the art should understand that the scope of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned concepts. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this invention.

[0059] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0060] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A vehicle violation monitoring method based on single Beidou video positioning, characterized in that: The method comprises: Obtaining Beidou positioning data generated in real time by a single Beidou positioning system for the target vehicle, wherein the Beidou positioning data includes the vehicle position coordinates and a timestamp; When the vehicle position coordinates fall within the preset illegal monitoring area electronic fence, the video stream collected in real time by the roadside video equipment covering the vehicle position coordinates is retrieved; Extracting a target video frame sequence matching the timestamp from the video stream, and identifying target behavior features of the target vehicle in the target video frame sequence based on a computer vision algorithm; The target behavior characteristics are compared with the preset illegal behavior rule library in real time. If the comparison is successful, an illegal evidence package is generated. The illegal evidence package includes Beidou positioning data, target video frame sequence and main illegal type identification.

2. The vehicle violation monitoring method based on single Beidou video positioning according to claim 1 is characterized in that: The method further comprises: Load the illegal monitoring area electronic fence boundary coordinate set pre-stored in the database; Calculate the spatial position relationship between the vehicle position coordinates and the electronic fence boundary coordinate set of the illegal monitoring area; Based on the calculation result, it is determined whether the vehicle position coordinates are within the electronic fence of the illegal monitoring area surrounded by the set of boundary coordinates of the electronic fence of the illegal monitoring area.

3. The vehicle violation monitoring method based on single Beidou video positioning according to claim 2 is characterized in that: The obtaining of Beidou positioning data generated in real time by a single Beidou positioning system for a target vehicle further includes: Receiving a Beidou positioning message sent by a vehicle-mounted Beidou terminal corresponding to the single Beidou positioning system; wherein the vehicle-mounted Beidou terminal generates the Beidou positioning message according to the received satellite signal of the single Beidou positioning system; Performing data parsing on the Beidou positioning message to extract the vehicle position coordinates and timestamp; Encapsulating the extracted vehicle position coordinates and the timestamp into structured Beidou positioning data; The Beidou positioning message is transmitted in real time through a wireless communication network, and the timestamp is derived from a single Beidou positioning system clock source.

4. The vehicle violation monitoring method based on single Beidou video positioning according to claim 3 is characterized in that: The step of retrieving a video stream collected in real time by a roadside video device covering the vehicle position coordinates further includes: Matching the target roadside video device identification covering the vehicle position coordinates from the video device geographic information database; A video stream retrieving instruction is sent to the corresponding roadside video device according to the target roadside video device identifier to receive the video stream returned in real time by the roadside video device.

5. The vehicle violation monitoring method based on single Beidou video positioning according to claim 4 is characterized in that: The identifying target behavior features of the target vehicle in the target video frame sequence based on a computer vision algorithm further includes: Based on the target detection algorithm, dynamically locate the image area of ​​the target vehicle in each frame image of the target video frame sequence; Extracting the spatiotemporal motion feature vector of the image region in each frame of image by motion analysis algorithm; All spatiotemporal motion feature vector sequences are converted into target behavior features through the behavior feature mapping function.

6. The vehicle violation monitoring method based on single Beidou video positioning according to claim 5 is characterized in that: The expression of the behavior feature mapping function is: Where, Indicates the target behavior characteristics, represents the mapping function, Represents the spatiotemporal motion feature vector of consecutive T frame images, Indicates the total number of frames in the target video frame sequence, represents the spatiotemporal motion feature vector of the t-th frame image, Indicates the number of preset illegal behavior feature dimensions, represents the dynamic weight parameter of the k-th dimension illegal behavior feature of the t-th frame, The spatial transformation matrix representing the k-th dimension of the illegal behavior feature, represents the bias vector of the k-th dimension illegal behavior feature, represents a nonlinear activation function; in, ; , represents the rate of change of the horizontal displacement of the center of mass of the target vehicle between consecutive frames; represents the horizontal coordinate of the center of mass pixel of the target vehicle in the tth frame, Represents the time interval from frame t to frame t+1; , represents the rate of change of vertical displacement of the center of mass of the target vehicle between consecutive frames; Indicates the pixel ordinate of the center of mass of the target vehicle in frame t; Indicates the offset of the vehicle direction angle between consecutive frames; represents the direction angle of the target vehicle in frame t; , represents the contour deformation coefficient of the target vehicle, is the contour point set, represents the contour point set of the target vehicle in the tth frame, is the base area; , represents the position entropy of the target vehicle, based on to Period calculation; Indicates the number of lane segmentation areas, Indicates the A segmented area, Indicates that the target vehicle is in The probability of occurrence of a segmented region.

7. The vehicle violation monitoring method based on single Beidou video positioning according to claim 6 is characterized in that: The method of comparing the target behavior characteristics with a preset illegal behavior rule library in real time and generating an illegal behavior evidence package if the comparison is successful further includes: Obtain target behavior characteristics The illegal behavior rule library Characteristic template vector of class illegal type Matching degree ,in , is the total number of violation types; Determine satisfaction Illegal type identification set ; Based on the preset violation type priority rules, from the violation type identification set Identify the main violation type in ; Generate an illegal evidence package, which includes the Beidou positioning data, the target video frame sequence and the main illegal type identifier ; Among them, the main violation type identification The formula for determining is: Where, Represents a set of illegal type identifiers, Indicates the number of violation types in the violation type identifier set, Represents the matching weight coefficient, represents the weight coefficient of the severity of the violation, Indicates the Preset severity scores for the offense type; Among them, the matching degree The calculation formula is: Where, Indicates the The feature template vector of the class violation type, Indicates the Type of illegal Characteristics of illegal behavior, The first Characteristics of illegal behavior, Indicates the The rule weight parameter that measures the characteristics of illegal behavior, , represents the eigenvalue difference threshold indicator, Indicates the The standard deviation tolerance parameter of the illegal behavior characteristics, represents the overall similarity enhancement coefficient, Indicates the The minimum match threshold for the class violation type.

8. A vehicle violation monitoring system based on single Beidou video positioning, characterized in that: The system comprises: An acquisition module is used to acquire Beidou positioning data generated in real time by a single Beidou positioning system for a target vehicle, wherein the Beidou positioning data includes vehicle position coordinates and a timestamp; A retrieval module is used to retrieve the video stream collected in real time by the roadside video equipment covering the vehicle position coordinates when the vehicle position coordinates fall within the preset illegal monitoring area electronic fence range; an identification module, configured to extract a target video frame sequence matching the timestamp from the video stream, and identify target behavior features of the target vehicle in the target video frame sequence based on a computer vision algorithm; The monitoring module is used to compare the target behavior characteristics with the preset illegal behavior rule library in real time. If the comparison is successful, a violation evidence package is generated. The violation evidence package contains Beidou positioning data, target video frame sequence and main violation type identification.

9. The vehicle violation monitoring system based on single Beidou video positioning according to claim 8 is characterized in that: The system further includes a judgment module; the judgment module is configured to: Load the illegal monitoring area electronic fence boundary coordinate set pre-stored in the database; Calculate the spatial position relationship between the vehicle position coordinates and the electronic fence boundary coordinate set of the illegal monitoring area; Based on the calculation result, it is determined whether the vehicle position coordinates are within the electronic fence of the illegal monitoring area surrounded by the set of boundary coordinates of the electronic fence of the illegal monitoring area.

10. The vehicle violation monitoring system based on single Beidou video positioning according to claim 9 is characterized in that: The acquisition module is specifically used for: Receiving a Beidou positioning message sent by a vehicle-mounted Beidou terminal corresponding to the single Beidou positioning system; wherein the vehicle-mounted Beidou terminal generates the Beidou positioning message according to the received satellite signal of the single Beidou positioning system; Performing data parsing on the Beidou positioning message to extract the vehicle position coordinates and the timestamp; Encapsulating the extracted vehicle position coordinates and the timestamp into structured Beidou positioning data; The Beidou positioning message is transmitted in real time through a wireless communication network, and the timestamp is derived from a single Beidou positioning system clock source.

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