Camera stake number determination method and device, electronic equipment and storage medium

Abstract

The invention is suitable for the technical field of road traffic monitoring, and provides a camera stake number determination method and device, electronic equipment and a storage medium. The camera stake number determination method comprises the following steps: firstly, identifying vehicle characteristics and camera passing time, determining a target vehicle, and further determining an average vehicle speed of the target vehicle according to a first time difference value of the target vehicle passing through at least two cameras with known stake numbers; and finally, according to the average vehicle speed and a second time difference between the camera with the unknown stake number and the camera with the known stake number, determining the stake number of the camera with the unknown stake number. Vehicle characteristics and a global driving track can be obtained according to a fusion perception technology, a target vehicle is determined, the target time when the target vehicle passes through the section of the camera with the unknown stake number is identified, finally, a target position corresponding to the target time in the target global driving track is searched, and the stake number of the camera with the unknown stake number is determined. Therefore, the real-time image of the camera displayed in the monitoring platform can be correctly associated with the road position.

Description

Technical Field

[0001] This application belongs to the technical field of road traffic monitoring, and particularly relates to a method, device, electronic device and storage medium for determining the camera mileage number. Background Art

[0002] In road traffic monitoring scenarios such as highways, tunnels, toll stations, etc., a large number of cameras are usually used, and the cameras are configured through a conventional video monitoring platform to display real-time images. However, some challenges are often faced during the camera configuration process, such as incorrect or missing installation location records, which result in the inability to accurately associate the monitoring images with specific road positions. Therefore, there is an urgent need to provide a method for determining the camera mileage number to ensure that the real-time camera images displayed on the monitoring platform can be correctly associated with their positions on the road. Summary of the Invention

[0003] Embodiments of this application provide a method, device, electronic device and storage medium for determining the camera mileage number, which can determine the camera mileage number to ensure that the real-time camera images displayed on the monitoring platform can be correctly associated with their positions on the road.

[0004] In a first aspect, embodiments of this application provide a method for determining the camera mileage number, and the method for determining the camera mileage number includes:

[0005] Extract the cross-sectional vehicle flow of all cameras on the target road, identify the passing time of each vehicle passing each camera and the vehicle characteristics, and determine the target vehicle;

[0006] Obtain the first time difference of the target vehicle passing at least two cameras with known mileage numbers, and determine the average vehicle speed of the target vehicle according to the at least two known mileage numbers and the first time difference;

[0007] Determine the mileage number of the camera with an unknown mileage number according to the average vehicle speed of the target vehicle, the second time difference of the target vehicle passing the camera with a known mileage number and the camera with an unknown mileage number, and the known mileage number; or,

[0008] Fuse and perceive the vehicle characteristics and the global driving trajectories of each vehicle on the target road, determine the target vehicle, and obtain the vehicle characteristics and the global driving trajectories of the target vehicle;

[0009] Extract the cross-sectional vehicle flow of all cameras on the target road, and identify the target time when the target vehicle passes the cross-section of the camera with an unknown mileage number according to the vehicle characteristics of the target vehicle;

[0010] Based on the target time when the target vehicle passes the cross-section of the camera with an unknown mileage number, find the target position corresponding to the target time in the target global driving trajectory of the target vehicle, and determine the mileage number of the camera with an unknown mileage number.

[0011] Optionally, by extracting the cross-sectional traffic flow of all cameras on the target road, identifying the passing time of each vehicle passing each camera and the vehicle characteristics, and determining the target vehicle, including:

[0012] Using AI technology, extract the cross-sectional traffic flow captured by all cameras on the target road;

[0013] Based on the cross-sectional traffic flow captured by all cameras, identify the passing time of each vehicle passing each camera and the vehicle characteristics;

[0014] Based on the vehicle characteristics of each vehicle, determine the target vehicle.

[0015] Optionally, before obtaining the first time difference of the target vehicle passing cameras at at least two known stake numbers, it further includes:

[0016] Obtain the stake numbers of cameras at at least two known stake numbers;

[0017] Based on the passing time of each vehicle passing each camera and the vehicle characteristics, determine the passing time of the target vehicle passing each camera and the vehicle characteristics;

[0018] Obtain the first time difference of the target vehicle passing cameras at at least two known stake numbers, including:

[0019] Based on the passing time of the target vehicle passing each camera and the stake numbers of cameras at at least two known stake numbers, obtain the first time difference of the target vehicle passing cameras at at least two known stake numbers.

[0020] Optionally, based on at least two known stake numbers and the first time difference, determine the average speed of the target vehicle, including

[0021] Based on at least two known stake numbers, determine the distance between at least two known stake numbers;

[0022] Based on the distance between at least two known stake numbers and the first time difference, determine the average speed of the target vehicle.

[0023] Optionally, based on the average speed of the target vehicle, the second time difference of the target vehicle passing cameras at known stake numbers and cameras at unknown stake numbers, and the known stake numbers, determine the stake number of the camera at the unknown stake number, including:

[0024] Based on the average speed of the target vehicle, the second time difference of the target vehicle passing cameras at known stake numbers and cameras at unknown stake numbers, determine the distance between the camera at the unknown stake number and the camera at the known stake number;

[0025] Based on the distance between the camera at the unknown stake number and the camera at the known stake number and the known stake numbers, determine the stake number of the camera at the unknown stake number.

[0026] Optionally, the global driving trajectory includes the driving time and the longitude and latitude coordinates corresponding to the driving time; fusing and perceiving the vehicle characteristics and the global driving trajectory of each vehicle on the target road to determine the target vehicle, and obtaining the vehicle characteristics and the global driving trajectory of the target vehicle, including:

[0027] Using the fusion perception technology, perceive the vehicle characteristics of each vehicle on the target road, the driving time of each vehicle on the target road, and the longitude and latitude coordinates corresponding to the driving time;

[0028] Determine the target vehicle according to the vehicle characteristics of each vehicle on the target road obtained by perception;

[0029] According to the target vehicle and the driving time of each vehicle on the target road and the longitude and latitude coordinates corresponding to the driving time, determine the driving time of the target vehicle on the target road and the longitude and latitude coordinates corresponding to the driving time.

[0030] Optionally, based on the target time when the target vehicle passes through the camera section with an unknown stake number, search for the target position corresponding to the target time in the global driving trajectory of the target vehicle, and determine the stake number of the camera with the unknown stake number, including:

[0031] Based on the target time when the target vehicle passes through the camera section with an unknown stake number, search for the longitude and latitude coordinates corresponding to the target time in the global driving trajectory of the target vehicle;

[0032] Convert the longitude and latitude coordinates corresponding to the target time into the stake number of the camera with the unknown stake number.

[0033] Optionally, the clocks of all cameras on the target road are synchronized; or the clocks of all cameras on the target road are synchronized with the clock of the fusion perception system.

[0034] In a second aspect, an embodiment of the present application provides a camera stake number determination device, and the camera stake number determination device includes:

[0035] A first target determination module, configured to identify the passing time and vehicle characteristics of each vehicle passing through each camera by performing cross-section traffic flow extraction on all cameras on the target road, and determine the target vehicle;

[0036] A vehicle speed determination module, configured to obtain a first time difference between the target vehicle passing through at least two cameras with known stake numbers, and determine the average vehicle speed of the target vehicle according to the at least two known stake numbers and the first time difference;

[0037] A first stake number determination module, configured to determine the stake number of the camera with the unknown stake number according to the average vehicle speed of the target vehicle, a second time difference between the target vehicle passing through the camera with the known stake number and the camera with the unknown stake number, and the known stake number; or,

[0038] The second target determination module is configured to fuse the vehicle characteristics and the global driving trajectories of each vehicle on the perceived target road, determine the target vehicle, and obtain the vehicle characteristics and the global driving trajectories of the target vehicle;

[0039] The time recognition module is configured to extract the cross-sectional vehicle flow of all cameras on the target road, and identify the target time when the target vehicle passes through the camera cross-section at an unknown stake number according to the vehicle characteristics of the target vehicle;

[0040] The second stake number determination module is configured to, based on the target time when the target vehicle passes through the camera cross-section at an unknown stake number, find the target position corresponding to the target time in the target global driving trajectory of the target vehicle, and determine the stake number of the camera at the unknown stake number.

[0041] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the camera stake number determination method as described above is implemented.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the camera stake number determination method as described above is implemented.

[0043] In a fifth aspect, an embodiment of the present application provides a computer program product, which when running on an electronic device, causes the electronic device to execute the camera stake number determination method as described above.

[0044] The beneficial effects of the embodiments of the present application compared with the prior art are as follows:

[0045] The embodiments of the present application can first identify the vehicle characteristics and the time of passing through the camera, determine the target vehicle, and then determine the average vehicle speed of the target vehicle according to the first time difference between the target vehicle passing through at least two cameras with known stake numbers and the known stake numbers, and finally determine the stake number of the camera at the unknown stake number according to the average vehicle speed and the second time difference between the camera at the unknown stake number and the camera at the known stake number; it is also possible to first determine the target vehicle according to the fusion perception technology, secondly, according to the vehicle characteristics and the global driving trajectory of the target vehicle, then identify the target time when the target vehicle passes through the camera cross-section at the unknown stake number, and finally find the target position corresponding to the target time in the target global driving trajectory of the target vehicle to determine the stake number of the camera at the unknown stake number. The above solutions ensure that the real-time camera images displayed on the monitoring platform can be correctly associated with their positions on the road by determining the stake numbers of the cameras at the unknown stake numbers. Description of the Drawings

[0046] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 It is a schematic flowchart of a method for determining the camera stake number provided by an embodiment of the present application;

[0048] Figure 2 It is a schematic diagram for calculating the stake number of a camera with an unknown stake number;

[0049] Figure 3 It is a schematic flowchart of a method for determining the camera stake number provided by another embodiment of the present application;

[0050] Figure 4 It is a schematic structural diagram of a device for determining the camera stake number provided by an embodiment of the present application;

[0051] Figure 5 It is a schematic structural diagram of a device for determining the camera stake number provided by another embodiment of the present application;

[0052] Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0053] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0054] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0055] It should also be understood that the term " / and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0056] As used in the specification of this application and the appended claims, the term "if" may be construed contextually as "when" or "once" or "in response to determining" or "in response to detecting". Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be construed contextually to mean "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]".

[0057] In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0058] Reference to "one embodiment" or "some embodiments" etc. described in the specification of this application means that a specific feature, structure or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0059] It should be understood that the magnitudes of the sequence numbers of the steps in this embodiment do not mean the order of execution is prior or subsequent. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0060] In road traffic monitoring scenarios such as highways, tunnels, toll stations, etc., a large number of cameras are usually used, and the cameras are configured through a conventional video monitoring platform to display real-time images. However, some challenges are often faced during the camera configuration process, such as incorrect recording or loss of the installation location, which results in the monitoring images being unable to be accurately associated with specific road locations. Therefore, there is an urgent need to provide a method for determining the camera stake number to ensure that the real-time images of the cameras displayed in the monitoring platform can be correctly associated with their positions on the road.

[0061] To solve the above problems, the present application provides a method for determining the camera stake number. First, it can identify vehicle features and the time when passing through the camera to determine the target vehicle. Then, based on the first time difference between the target vehicle passing through at least two cameras with known stake numbers and the known stake numbers, it can determine the average speed of the target vehicle. Finally, based on the average speed and the second time difference between passing through the camera with an unknown stake number and the camera with a known stake number, it can determine the stake number of the camera with the unknown stake number. It can also first determine the target vehicle according to the fusion perception technology, secondly, based on the vehicle features and the global driving trajectory of the target vehicle, then identify the target time when the target vehicle passes through the cross-section of the camera with an unknown stake number, and finally find the target position corresponding to the target time in the target global driving trajectory of the target vehicle to determine the stake number of the camera with the unknown stake number. The above solution ensures that the real-time camera images displayed on the monitoring platform can be correctly associated with their positions on the road by determining the stake number of the camera with the unknown stake number.

[0062] The following will describe in detail the method, device, electronic device, storage medium, and computer program for determining the camera stake number provided by the present application with reference to the accompanying drawings.

[0063] Figure 1 The flowchart of a method for determining the camera stake number provided by an embodiment of the present application is shown.

[0064] Step 101: Extract the cross-sectional traffic flow of all cameras on the target road, identify the passing time and vehicle features of each vehicle passing through each camera, and determine the target vehicle.

[0065] It should be noted that the method for determining the camera stake number in the embodiment of the present application can be executed by the device for determining the camera stake number in the embodiment of the present application. The device for determining the camera stake number in the embodiment of the present application can be configured in any electronic device to execute the method for determining the camera stake number in the embodiment of the present application. For example, the device for determining the camera stake number in the embodiment of the present application can be configured in an electronic device installed with monitoring platform software to determine the method for determining the camera stake number and ensure that the real-time camera images displayed on the monitoring platform can be correctly associated with their positions on the road.

[0066] In the embodiment of the present application, extracting the cross-sectional traffic flow of all cameras on the target road may refer to the process of analyzing the images and videos captured by the cameras on the target road to identify and count the number of vehicles passing through a specific cross-section.

[0067] Specifically, computer vision algorithms (such as convolutional neural networks in deep learning or other object detection algorithms) are used to process the images or videos captured by the camera to detect the vehicles on the road and output the position information of each detected vehicle. Then, the area of the target section is determined. Based on the vehicle position information and the area definition, the vehicles that do not pass through the target section are filtered out, and only the vehicles within the target area are retained. Each vehicle passing through the target section is counted, which can be achieved by simply calculating the number of times the vehicle passes through the area. Finally, the statistical data of each camera are integrated to form global traffic flow statistical information, and the traffic flow statistical information is displayed in a visual form, such as charts, graphs, or heat maps on a map, so that users can intuitively understand the traffic flow situation. The embodiment of the present application can display the information of cross-section traffic flow extraction in the form of a plan view.

[0068] After obtaining the information of cross-section traffic flow extraction according to the above method, the passing time and vehicle characteristics of each vehicle passing through each camera can be identified based on the information of cross-section traffic flow extraction to determine the target vehicle for subsequent calculation of the camera station number.

[0069] In a possible implementation manner, by performing cross-section traffic flow extraction on all cameras on the target road, identifying the passing time and vehicle characteristics of each vehicle passing through each camera, and determining the target vehicle, it includes:

[0070] Using AI technology to extract the cross-section traffic flow captured by all cameras on the target road;

[0071] Based on the cross-section traffic flow captured by all cameras, identifying the passing time and vehicle characteristics of each vehicle passing through each camera;

[0072] Determining the target vehicle according to the vehicle characteristics of each vehicle.

[0073] Among them, AI technology may refer to artificial intelligence technology, including machine learning, deep learning, natural language processing, computer vision, reinforcement learning, etc.

[0074] In the embodiment of the present application, after identifying the passing time and vehicle characteristics of each vehicle passing through each camera, a vehicle with more obvious vehicle characteristics can be selected as the target vehicle according to the vehicle characteristics of each vehicle. The vehicle includes color and vehicle type. For example, a vehicle with a color of green can be selected as the target vehicle.

[0075] In the embodiment of the present application, the vehicle characteristics required to determine the target vehicle can be set by the user, and the present application does not make any limitations in this regard.

[0076] Step 102: Obtain the first time difference of the target vehicle passing through at least two cameras with known station numbers, and determine the average vehicle speed of the target vehicle according to the at least two known station numbers and the first time difference.

[0077] In the embodiment of the present application, after extracting the cross-sectional traffic flow of all cameras as described above and identifying the passing time of each vehicle passing each camera, the first time difference of the target vehicle passing two cameras with known mileage can be obtained by searching. Since the distance between the two cameras with known mileage is known, the average speed of the target vehicle can be determined according to the distance between the mileages and the first time difference of passing.

[0078] In a possible implementation manner, before obtaining the first time difference of the target vehicle passing at least two cameras with known mileage, it further includes:

[0079] Obtain the mileages of at least two cameras with known mileage;

[0080] According to the passing time of each vehicle passing each camera and the vehicle characteristics, determine the passing time of the target vehicle passing each camera and the vehicle characteristics;

[0081] Obtaining the first time difference of the target vehicle passing at least two cameras with known mileage includes:

[0082] According to the passing time of the target vehicle passing each camera and the mileages of at least two cameras with known mileage, obtain the first time difference of the target vehicle passing at least two cameras with known mileage.

[0083] In the embodiment of the present application, the mileages of at least two cameras with known mileage can be obtained. After determining the target vehicle, the passing time of the target vehicle passing each camera and the vehicle characteristics can be found from the passing time of each vehicle passing each camera and the vehicle characteristics, and then, according to the known mileage, the first time difference of the target vehicle passing the camera with known mileage can be obtained.

[0084] In a possible implementation manner, determining the average speed of the target vehicle according to at least two known mileages and the first time difference includes:

[0085] According to at least two known mileages, determine the distance between at least two known mileages;

[0086] According to the distance between at least two known mileages and the first time difference, determine the average speed of the target vehicle.

[0087] In the embodiment of the present application, since the mileage represents the position on the target road, when the known mileages of two cameras are obtained, the distance between the mileages of the two cameras can be determined. Then, according to the distance between the mileages of the two cameras and the time of the target vehicle passing the two cameras, the average speed of the target vehicle on the target road can be obtained.

[0088] Step 103: Determine the stake number of the camera with an unknown stake number based on the average speed of the target vehicle, the second time difference between the target vehicle passing the cameras with known stake numbers and the camera with an unknown stake number, and the known stake number.

[0089] In the embodiment of the present application, the stake number of the camera with an unknown stake number can be deduced based on the average speed of the target vehicle, the second time difference between the target vehicle passing the cameras with known stake numbers and the camera with an unknown stake number, and the known stake number of the camera, that is, the position of the camera with an unknown stake number on the target road.

[0090] In a possible implementation manner, determining the stake number of the camera with an unknown stake number based on the average speed of the target vehicle, the second time difference between the target vehicle passing the cameras with known stake numbers and the camera with an unknown stake number, and the known stake number includes:

[0091] Determine the distance between the camera with an unknown stake number and the camera with a known stake number according to the average speed of the target vehicle and the second time difference between the target vehicle passing the cameras with known stake numbers and the camera with an unknown stake number;

[0092] Determine the stake number of the camera with an unknown stake number according to the distance between the camera with an unknown stake number and the camera with a known stake number and the known stake number.

[0093] Exemplarily, as Figure 2 shown, a schematic diagram of stake number calculation for the camera with an unknown stake number is shown. Cameras with two known stake numbers are installed at positions 1 and 2 respectively, and a camera with an unknown stake number is installed at position x. The distance between positions 1 and 2 is D1, and the first time difference is t1. Therefore, through D1 / t1 = v1, the average speed v1 can be obtained. After obtaining the average speed v1, combined with the second time difference t2 between the target vehicle passing between positions 1 and x, the distance D2 between positions 1 and x can be obtained. According to D2 and the stake number at position 1, the stake number of the camera at position x can be deduced.

[0094] In the embodiment of the present application, to ensure the accurate calculation of the stake number of the camera with an unknown stake number, it is necessary to ensure that the clocks of all cameras on the target road are synchronized.

[0095] In the embodiment of the present application, first identify the vehicle characteristics and the time of passing the camera to determine the target vehicle. Then, determine the average speed of the target vehicle according to the first time difference between the target vehicle passing at least two cameras with known stake numbers and the known stake numbers. Finally, determine the stake number of the camera with an unknown stake number according to the average speed and the second time difference between passing the camera with an unknown stake number and the camera with a known stake number. By determining the stake number of the camera with an unknown stake number in the embodiment of the present application, it is ensured that the real-time images of the cameras displayed in the monitoring platform can be correctly associated with their positions on the road.

[0096] Figure 3 The figure shows a schematic flowchart of a method for determining the camera stake number provided by another embodiment of the present application.

[0097] Step 301: Integrate the vehicle features and the global driving trajectories of each vehicle on the target road to determine the target vehicle, and obtain the vehicle features and the global driving trajectories of the target vehicle.

[0098] It should be noted that the method for determining the camera stake number in the embodiments of the present application can be executed by the device for determining the camera stake number in the embodiments of the present application. The device for determining the camera stake number in the embodiments of the present application can be configured in any electronic device to execute the method for determining the camera stake number in the embodiments of the present application. For example, the device for determining the camera stake number in the embodiments of the present application can be configured in an electronic device installed with monitoring platform software to determine the method for determining the camera stake number, so as to ensure that the real-time camera images displayed in the monitoring platform can be correctly associated with their positions on the road.

[0099] Among them, the monitoring platform can be combined with the fusion perception system to obtain the perception data of the fusion perception system.

[0100] In the embodiments of the present application, the fusion perception may include the following aspects:

[0101] Sensor data fusion: Integrate the information from different types of sensors. Common sensors include cameras, lidar, millimeter-wave radars, ultrasonic sensors, etc. These sensors each have unique perception capabilities. By fusing their data, their limitations can be compensated, and the comprehensiveness of environmental perception can be improved.

[0102] Environmental map fusion: Integrate the information sensed by the vehicle in real time with the pre-constructed map data. This helps the vehicle better understand its own position, road structure, intersections, parking spaces, etc. The environmental map can be a high-precision map containing information such as road geometry and traffic signs.

[0103] Object recognition and tracking: Sense the surrounding vehicles, pedestrians, obstacles and other objects by performing object recognition and tracking on the sensor data.

[0104] Semantic segmentation: Use technologies such as deep learning to perform semantic segmentation on the sensor data, and divide the scene into different semantic regions, such as roads, sidewalks, lanes, buildings, etc. This helps to understand the road environment more precisely.

[0105] By using the above fusion perception technologies, the vehicle features and the global driving trajectories of each vehicle on the target road can be obtained, and then the target vehicle can be determined, and the vehicle features and the global driving trajectories of the target vehicle can be obtained.

[0106] In a possible implementation, the global driving trajectory includes driving time and the corresponding longitude and latitude coordinates at the driving time; fusing and perceiving the vehicle characteristics of each vehicle on the target road and the global driving trajectory to determine the target vehicle, and obtaining the vehicle characteristics and global driving trajectory of the target vehicle, including:

[0107] Using the fusion perception technology, perceive the vehicle characteristics of each vehicle on the target road, the driving time of each vehicle on the target road, and the corresponding longitude and latitude coordinates at the driving time;

[0108] Determine the target vehicle according to the vehicle characteristics of each vehicle on the target road obtained by perception;

[0109] According to the target vehicle and the driving time of each vehicle on the target road and the corresponding longitude and latitude coordinates at the driving time, determine the driving time of the target vehicle on the target road and the corresponding longitude and latitude coordinates at the driving time.

[0110] In the embodiment of the present application, according to the vehicle characteristics of each vehicle on the target road obtained by perception, a vehicle with more obvious features can be selected as the target vehicle. The vehicle includes color and model. For example, a vehicle with a color of green can be selected as the target vehicle.

[0111] In the embodiment of the present application, the vehicle characteristics required to determine the target vehicle can be set by the user, and the present application does not make any limitations thereto.

[0112] Step 302: Extract the cross-sectional traffic flow of all cameras on the target road, and identify the target time when the target vehicle passes through the camera cross-section with an unknown stake number according to the vehicle characteristics of the target vehicle.

[0113] In the embodiment of the present application, after the target vehicle is determined, the cross-sectional traffic flow of all cameras on the target road can be extracted to obtain the cross-sectional traffic flow of the target vehicle passing through all cameras, and then the target time when the target vehicle passes through the camera cross-section with an unknown stake number can be identified, so as to find the position of the target vehicle at the same time in the above-mentioned target global driving trajectory, and then determine the stake number of the camera with the unknown stake number.

[0114] Step 303: Based on the target time when the target vehicle passes through the camera cross-section with an unknown stake number, find the target position corresponding to the target time in the target global driving trajectory of the target vehicle, and determine the stake number of the camera with the unknown stake number.

[0115] In the embodiment of the present application, find the target position corresponding to the target time in the target global driving trajectory, and determine the installation position of the camera with the unknown stake number through the obtained target position, and then the stake number of the camera with the unknown stake number can be determined.

[0116] In a possible implementation, based on the target time when the target vehicle passes through the camera section with an unknown stake number, find the target position corresponding to the target time in the global driving trajectory of the target vehicle, and determine the stake number of the camera with the unknown stake number, including:

[0117] Based on the target time when the target vehicle passes through the camera section with an unknown stake number, find the longitude and latitude coordinates corresponding to the target time in the global driving trajectory of the target vehicle;

[0118] Convert the longitude and latitude coordinates corresponding to the target time into the stake number of the camera with the unknown stake number.

[0119] In the embodiments of the present application, the position information in the driving trajectory is represented by longitude and latitude coordinates. Therefore, when finding the target position corresponding to the target time in the target global driving trajectory, the longitude and latitude coordinates corresponding to the target position can be obtained. By converting the longitude and latitude coordinates into the stake number of the camera, the stake number of the camera with the unknown stake number can be obtained.

[0120] In the embodiments of the present application, to ensure the accurate calculation of the stake number of the camera with the unknown stake number, it is necessary to ensure that the clocks of all cameras on the target road are synchronized with the clock of the fusion perception system.

[0121] In the embodiments of the present application, first, the target vehicle is determined according to the fusion perception technology. Secondly, according to the vehicle characteristics and global driving trajectory of the target vehicle. Then, the target time when the target vehicle passes through the camera section with the unknown stake number is identified. Finally, the target position corresponding to the target time in the target global driving trajectory of the target vehicle is found, and the stake number of the camera with the unknown stake number is determined. By determining the stake number of the camera with the unknown stake number in the embodiments of the present application, it is ensured that the real-time camera images displayed on the monitoring platform can be correctly associated with their positions on the road.

[0122] Corresponding to the method for determining the camera stake number in the above embodiments, Figure 4 FIG. shows a schematic structural diagram of a camera stake number determination device provided in an embodiment of the present application. For the sake of simplicity, only the parts related to the embodiments of the present application are shown.

[0123] See Figure 4 , the camera stake number determination device 400 includes:

[0124] The first target determination module 401 is configured to extract the cross-section traffic flow of all cameras on the target road, identify the passing time and vehicle characteristics of each vehicle passing through each camera, and determine the target vehicle;

[0125] The vehicle speed determination module 402 is configured to obtain the first time difference when the target vehicle passes through at least two cameras with known stake numbers, and determine the average vehicle speed of the target vehicle according to the at least two known stake numbers and the first time difference;

[0126] The first stake number determination module 403 is configured to determine the stake number of the camera with an unknown stake number according to the average speed of the target vehicle, the second time difference between the camera with a known stake number and the camera with an unknown stake number that the target vehicle passes through, and the known stake number.

[0127] In the embodiment of the present application, the first target determination module 401 may specifically include the following units:

[0128] The cross-section traffic flow extraction unit is configured to extract the cross-section traffic flow captured by all cameras on the target road using AI technology;

[0129] The recognition unit is configured to recognize the passing time and vehicle characteristics of each vehicle passing through each camera according to the cross-section traffic flow captured by all cameras;

[0130] The target vehicle determination unit is configured to determine the target vehicle according to the vehicle characteristics of each vehicle.

[0131] In the embodiment of the present application, the camera stake number determination device 400 may specifically further include:

[0132] The stake number acquisition module is configured to acquire the stake numbers of at least two cameras with known stake numbers;

[0133] The third target determination module is configured to determine the passing time and vehicle characteristics of the target vehicle passing through each camera according to the passing time of each vehicle passing through each camera and the vehicle characteristics;

[0134] Correspondingly, the vehicle speed determination module 402 may specifically include the following units:

[0135] The time difference acquisition unit is configured to acquire the first time difference between the target vehicle passing through at least two cameras with known stake numbers according to the passing time of the target vehicle passing through each camera and the stake numbers of at least two cameras with known stake numbers.

[0136] In the embodiment of the present application, the vehicle speed determination module 402 may specifically further include the following units:

[0137] The first distance determination unit is configured to determine the distance between at least two known stake numbers according to at least two known stake numbers;

[0138] The average vehicle speed determination unit is configured to determine the average vehicle speed of the target vehicle according to the distance between at least two known stake numbers and the first time difference.

[0139] In the embodiment of the present application, the stake number determination module 403 may specifically further include the following units:

[0140] A second distance determination unit, configured to determine the distance between a camera at an unknown stake number and a camera at a known stake number according to the average vehicle speed of the target vehicle and the second time difference between the target vehicle passing through the camera at the known stake number and the camera at the unknown stake number;

[0141] A stake number determination unit, configured to determine the stake number of the camera at the unknown stake number according to the distance between the camera at the unknown stake number and the camera at the known stake number and the known stake number.

[0142] In the embodiment of the present application, the clocks of all cameras on the target road are synchronized.

[0143] Corresponding to the camera stake number determination method in the above embodiment, Figure 5 FIG. shows a schematic structural diagram of a camera stake number determination device provided in another embodiment of the present application. For the sake of illustration, only the parts related to the embodiment of the present application are shown.

[0144] See Figure 5 , the camera stake number determination device 500 includes:

[0145] A second target determination module 501, configured to fuse and sense the vehicle characteristics and the global driving trajectory of each vehicle on the target road, determine the target vehicle, and obtain the vehicle characteristics and the global driving trajectory of the target vehicle;

[0146] A time recognition module 502, configured to extract cross-sectional vehicle flows for all cameras on the target road, and recognize the target time when the target vehicle passes through the cross-section of the camera at the unknown stake number according to the vehicle characteristics of the target vehicle;

[0147] A second stake number determination module 503, configured to, based on the target time when the target vehicle passes through the cross-section of the camera at the unknown stake number, find the target position corresponding to the target time in the target global driving trajectory of the target vehicle, and determine the stake number of the camera at the unknown stake number.

[0148] In the embodiment of the present application, the global driving trajectory includes the driving time and the longitude and latitude coordinates corresponding to the driving time. The second target determination module 501 may specifically include the following units:

[0149] A fusion perception unit, configured to use the fusion perception technology to sense the vehicle characteristics of each vehicle on the target road and the driving time of each vehicle on the target road and the longitude and latitude coordinates corresponding to the driving time;

[0150] A target vehicle determination unit, configured to determine the target vehicle according to the vehicle characteristics of each vehicle on the target road sensed;

[0151] A coordinate determination unit, configured to determine the driving time of the target vehicle on the target road and the longitude and latitude coordinates corresponding to the driving time according to the driving time of the target vehicle and each vehicle on the target road and the longitude and latitude coordinates corresponding to the driving time.

[0152] In the embodiment of the present application, the second stake number determination module 503 may specifically include the following units:

[0153] A search unit, configured to search for the longitude and latitude coordinates corresponding to the target time in the global driving trajectory of the target vehicle based on the target time when the target vehicle passes through the camera section with an unknown stake number;

[0154] A conversion unit, configured to convert the longitude and latitude coordinates corresponding to the target time into the stake number of the camera with an unknown stake number.

[0155] In the embodiment of the present application, the clocks of all cameras on the target road are synchronized with the clock of the fusion perception system.

[0156] It should be noted that for the information interaction, execution process, etc. between the above-mentioned devices / units, since they are based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details will not be elaborated here.

[0157] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment, and details will not be elaborated here.

[0158] See Figure 6 , which shows a schematic structural diagram of an electronic device provided in an embodiment of the present application. As Figure 6 shown, the electronic device 600 in this embodiment includes: at least one processor 610 ( Figure 6 only one is shown in ), a memory 620, and a computer program 621 stored in the memory 620 and executable on the at least one processor 610. When the processor 610 executes the computer program 621, the steps in the method embodiment of the above camera stake number determination method are implemented.

[0159] It should be noted that the electronic device 600 may refer to computing devices such as desktop computers, notebooks, palm computers, and cloud servers. The electronic device may include, but is not limited to, a processor 610 and a memory 620. Those skilled in the art can understand that Figure 6 merely examples of the electronic device 600, which do not constitute a limitation on the electronic device 600, may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0160] The so-called processor 610 may be a central processing unit (CPU), and the processor 610 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0161] The memory 620 may be an internal storage unit of the electronic device 600 in some embodiments, such as the hard disk or memory of the electronic device 600. The memory 620 may also be an external storage device of the electronic device 600 in some other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 600. Further, the memory 620 may also include both the internal storage unit and the external storage device of the electronic device 600. The memory 620 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program, etc. The memory 620 may also be used to temporarily store data that has been output or will be output.

[0162] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0163] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0164] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0165] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0167] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of this application, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0168] To implement all or part of the processes in the above-described embodiment methods of this application, it can also be completed by a computer program product. When the computer program product runs on an electronic device, the electronic device can be made to execute the steps in the above-described various method embodiments when executed.

[0169] The above-described embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for determining the stake number of a camera, characterized in that, The method for determining the camera stake number includes: By extracting the cross-sectional traffic flow of all cameras on the target road, identifying the passing time and vehicle characteristics of each vehicle passing each camera, and determining the target vehicle; Obtaining the first time difference of the target vehicle passing at least two cameras with known stake numbers, and determining the average speed of the target vehicle according to the at least two known stake numbers and the first time difference; Determining the stake number of the camera with the unknown stake number according to the average speed of the target vehicle, the second time difference of the target vehicle passing the cameras with known stake numbers and the camera with the unknown stake number, and the known stake number; or, Fusing and perceiving the vehicle characteristics and global driving trajectories of each vehicle on the target road, determining the target vehicle, and obtaining the vehicle characteristics and global driving trajectories of the target vehicle; By extracting the cross-sectional traffic flow of all cameras on the target road, and identifying the target time of the target vehicle passing the cross-section of the camera with the unknown stake number according to the vehicle characteristics of the target vehicle; Based on the target time of the target vehicle passing the cross-section of the camera with the unknown stake number, searching for the target position corresponding to the target time in the target global driving trajectory of the target vehicle, and determining the stake number of the camera with the unknown stake number.

2. The method for determining the camera stake number according to claim 1, characterized in that, The step of extracting the cross-sectional traffic flow of all cameras on the target road, identifying the passing time and vehicle characteristics of each vehicle passing each camera, and determining the target vehicle includes: Using AI technology to extract the cross-sectional traffic flow captured by all cameras on the target road; Identifying the passing time and vehicle characteristics of each vehicle passing each camera according to the cross-sectional traffic flow captured by all cameras; Determining the target vehicle according to the vehicle characteristics of each vehicle.

3. The method for determining the camera stake number according to claim 1, wherein, Before obtaining the first time difference of the target vehicle passing at least two cameras with known stake numbers, it further includes: Obtaining the stake numbers of at least two cameras with known stake numbers; Determining the passing time and vehicle characteristics of the target vehicle passing each camera according to the passing time and vehicle characteristics of each vehicle passing each camera; The step of obtaining the first time difference of the target vehicle passing at least two cameras with known stake numbers includes: Obtaining the first time difference of the target vehicle passing at least two cameras with known stake numbers according to the passing time of the target vehicle passing each camera and the stake numbers of the at least two cameras with known stake numbers.

4. The method for determining the camera stake number according to claim 3, characterized in that, The step of determining the average speed of the target vehicle according to the at least two known stake numbers and the first time difference includes Determining the distance between the at least two known stake numbers according to the at least two known stake numbers; Determining the average speed of the target vehicle according to the distance between the at least two known stake numbers and the first time difference.

5. The method for determining the camera stake number according to claim 1, characterized in that, The step of determining the stake number of the camera with the unknown stake number according to the average speed of the target vehicle, the second time difference of the target vehicle passing the cameras with known stake numbers and the camera with the unknown stake number, and the known stake number includes: Determine the distance between the camera with an unknown stake number and the camera with a known stake number according to the average vehicle speed of the target vehicle and the second time difference between the camera with the known stake number and the camera with the unknown stake number that the target vehicle passes through; Determine the stake number of the camera with the unknown stake number according to the distance between the camera with the unknown stake number and the camera with the known stake number and the known stake number.

6. The method for determining the camera stake number according to claim 1, characterized in that, The global driving trajectory includes the driving time and the longitude and latitude coordinates corresponding to the driving time; the fusion perception of the vehicle characteristics and the global driving trajectory of each vehicle on the target road to determine the target vehicle and obtain the vehicle characteristics and the global driving trajectory of the target vehicle includes: Use the fusion perception technology to perceive the vehicle characteristics of each vehicle on the target road, the driving time of each vehicle on the target road, and the longitude and latitude coordinates corresponding to the driving time; Determine the target vehicle according to the vehicle characteristics of each vehicle on the target road obtained by perception; Determine the driving time of the target vehicle on the target road and the longitude and latitude coordinates corresponding to the driving time according to the target vehicle, the driving time of each vehicle on the target road, and the longitude and latitude coordinates corresponding to the driving time.

7. The method for determining the camera stake number according to claim 6, wherein, The method of finding the target position corresponding to the target time in the global driving trajectory of the target vehicle based on the target time when the target vehicle passes through the camera section with an unknown stake number and determining the stake number of the camera with the unknown stake number includes: Based on the target time when the target vehicle passes through the camera section with an unknown stake number, find the longitude and latitude coordinates corresponding to the target time in the global driving trajectory of the target vehicle; Convert the longitude and latitude coordinates corresponding to the target time into the stake number of the camera with the unknown stake number.

8. The method for determining the camera stake number according to any one of claims 1 to 7, characterized in that Synchronize the clocks of all cameras on the target road; or synchronize the clocks of all cameras on the target road with the clock of the fusion perception system.

9. A device for determining the camera stake number, characterized in that The camera stake number determination device includes: A first target determination module, configured to identify the passing time and vehicle characteristics of each vehicle passing through each camera by performing cross-sectional traffic flow extraction on all cameras on the target road, and determine the target vehicle; A vehicle speed determination module, configured to obtain the first time difference between the target vehicle passing through at least two cameras with known stake numbers, and determine the average vehicle speed of the target vehicle according to the at least two known stake numbers and the first time difference; A first stake number determination module, configured to determine the stake number of the camera with the unknown stake number according to the average vehicle speed of the target vehicle, the second time difference between the camera with the known stake number and the camera with the unknown stake number that the target vehicle passes through, and the known stake number; or, A second target determination module, configured to fuse and perceive the vehicle characteristics and the global driving trajectory of each vehicle on the target road to determine the target vehicle, and obtain the vehicle characteristics and the global driving trajectory of the target vehicle; A time identification module, configured to perform cross-sectional traffic flow extraction on all cameras on the target road, and identify the target time when the target vehicle passes through the camera section with an unknown stake number according to the vehicle characteristics of the target vehicle; The second mileage determination module is configured to find a target position corresponding to the target time in the target global driving trajectory of the target vehicle based on the target time when the target vehicle passes through the camera section of the unknown mileage, and determine the mileage of the camera at the unknown mileage.

10. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the method according to any one of claims 1 to 8 is implemented.

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