Vehicle detection tracking scheduling method and device, electronic equipment and storage medium

Abstract

The invention provides a vehicle detection, tracking and scheduling method and device, electronic equipment and a storage medium, the method and device are applied to a target area, a plurality of cameras are arranged in the target area, and the method comprises the steps that when a vehicle enters the target area, declaration data, images and collection data of the vehicle are acquired; inputting the image of the vehicle into a preset re-identification model, and outputting target re-identification information of the vehicle; binding the target re-identification information, the declaration data and the collection data of the vehicle; when the vehicle runs in the target area, determining a plurality of target images including the vehicle from images shot by a plurality of cameras in the target area based on the target re-identification information; and synthesizing the plurality of target images into a video that the vehicle runs in the target area. In the mode, the visual information acquired by the camera can be classified and integrated, so that the vehicles entering the target area can be identified and tracked as many as possible, and the management process of the target area is optimized.

Description

Technical Field

[0001] The present invention relates to the technical fields of artificial intelligence and visual recognition, and particularly to a method, device, electronic device and storage medium for detecting, tracking and scheduling vehicles. Background Art

[0002] With the acceleration of economic development and urbanization, the number of vehicles passing through trade has also increased day by day. Traditional traffic management methods can no longer meet the regulatory requirements. Traditional traffic management solutions generally only manage at the checkpoints, and cannot track the driving trajectories of vehicles after they enter the park. There may be problems such as stopping at a certain position for a long time after entering the area without entering the yard or inspection area, or opening the container to exchange goods after entering the area. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, device, electronic device and storage medium for detecting, tracking and scheduling vehicles, so as to classify and integrate the visual information obtained by the camera, and thus identify and track as many vehicles entering the target area as possible, so as to optimize the management process of the target area.

[0004] In a first aspect, an embodiment of the present invention provides a method for detecting, tracking and scheduling vehicles, which is applied to a target area, and a plurality of cameras are arranged in the target area. The method includes: obtaining the declaration data, image and acquisition data of the vehicle when the vehicle enters the target area; wherein, the acquisition data includes: license plate information and weight data; inputting the image of the vehicle into a preset re-identification model to output the target re-identification information of the vehicle; binding the target re-identification information, declaration data and acquisition data of the vehicle; when the vehicle is driving in the target area, determining a plurality of target images including the vehicle from the images captured by the plurality of cameras in the target area based on the target re-identification information; and synthesizing the plurality of target images into a video of the vehicle driving in the target area.

[0005] In an optional embodiment of the present application, the step of obtaining the declaration data, image and acquisition data of the vehicle when the vehicle enters the target area includes: reading the driver's IC card to obtain the declaration data of the vehicle when the vehicle enters the entrance checkpoint of the target area; collecting the image of the vehicle through the camera arranged at the entrance checkpoint; collecting the license plate information of the vehicle through the license plate detection device arranged at the entrance checkpoint; and collecting the weight data of the vehicle through the weight detection device arranged at the entrance checkpoint.

[0006] In an alternative embodiment of the present application, the step of determining a plurality of target images including vehicles from the images captured by multiple cameras in the target area based on the target re-identification information includes: inputting the images captured by the multiple cameras in the target area into a re-identification model, and outputting the re-identification information of each vehicle in each image; if there is a re-identification information in the image that is the target re-identification information, then the image is a target image including a vehicle.

[0007] In an alternative embodiment of the present application, after the step of synthesizing the plurality of target images into a video of the vehicle traveling in the target area, the method further includes: determining the driving trajectory of the vehicle in the target area based on the video.

[0008] In an alternative embodiment of the present application, the step of determining the driving trajectory of the vehicle in the target area based on the video includes: inputting the video into a preset target detection model, and outputting the bounding box of the vehicle; determining the vehicle in the video based on the bounding box of the vehicle; and determining the driving trajectory of the vehicle in the target area based on the vehicle in the video by means of target tracking.

[0009] In an alternative embodiment of the present application, after the step of synthesizing the plurality of target images into a video of the vehicle traveling in the target area, the method further includes: in response to an operation of the user clicking on the vehicle in the video, displaying the target re-identification information, declaration data, and acquisition data of the vehicle.

[0010] In an alternative embodiment of the present application, after the step of synthesizing the plurality of target images into a video of the vehicle traveling in the target area, the method further includes: obtaining a query instruction and determining the video corresponding to the query instruction; wherein the query instruction includes: the re-identification information or license plate information of the vehicle.

[0011] In a second aspect, an embodiment of the present invention further provides a vehicle detection, tracking, and scheduling device, which is applied to a target area where multiple cameras are arranged. The device includes: a data acquisition and collection module, configured to acquire the declaration data, images, and acquisition data of the vehicle when the vehicle enters the target area; wherein the acquisition data includes: license plate information and weight data; a re-identification module, configured to input the image of the vehicle into a preset re-identification model and output the target re-identification information of the vehicle; an information and data binding module, configured to bind the target re-identification information, declaration data, and acquisition data of the vehicle; a target image determination module, configured to determine a plurality of target images including the vehicle from the images captured by multiple cameras in the target area based on the target re-identification information when the vehicle is traveling in the target area; and a video synthesis module, configured to synthesize the plurality of target images into a video of the vehicle traveling in the target area.

[0012] In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor and a memory. The memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the above-mentioned vehicle detection, tracking, and scheduling method.

[0013] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the above-mentioned vehicle detection, tracking, and scheduling method.

[0014] The embodiments of the present invention bring the following beneficial effects: The embodiments of the present invention provide a vehicle detection, tracking, and scheduling method, device, electronic device, and storage medium. When a vehicle enters a target area, declaration data, images, and acquisition data of the vehicle are obtained; among them, the acquisition data includes license plate information and weight data; the image of the vehicle is input into a preset re-identification model to output target re-identification information of the vehicle; the target re-identification information, declaration data, and acquisition data of the vehicle are bound; when the vehicle is driving in the target area, based on the target re-identification information, multiple target images including the vehicle are determined from the images captured by multiple cameras in the target area; the multiple target images are synthesized into a video of the vehicle driving in the target area. In this way, by classifying and integrating the visual information obtained by the camera, as many vehicles entering the target area as possible can be identified and tracked, thereby optimizing the management process of the target area.

[0015] Other features and advantages of the present disclosure will be described in the following description of the specification, or some features and advantages can be inferred from the description of the specification without doubt, or can be known by implementing the above technologies of the present disclosure.

[0016] To make the above objects, features, and advantages of the present disclosure more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of a vehicle detection, tracking, and scheduling method provided by an embodiment of the present invention; Figure 2Flow chart of another vehicle detection, tracking and scheduling method provided by an embodiment of the present invention; Figure 3 Structural schematic diagram of a vehicle detection, tracking and scheduling device provided by an embodiment of the present invention; Figure 4 Structural schematic diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0019] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Currently, with the acceleration of economic development and urbanization, the number of vehicles passing through trade has also increased day by day, and traditional traffic management methods can no longer meet the supervision requirements. Traditional traffic management solutions generally only manage at the checkpoints and cannot track the driving trajectories of vehicles after they enter the park. Problems may occur such as stopping at a certain position for a long time after entering the area without entering the yard or inspection area, or opening the container to swap goods after entering the area.

[0021] With the rapid development of deep learning and computer vision technologies, the accuracy and efficiency of vehicle detection and tracking have been greatly improved. The reduction of hardware costs and the improvement of computing power have made it possible for computer vision methods to be applied in large-scale applications. The disclosure of a large amount of traffic video data and public data sets provides rich resources for training and validating computer vision algorithms. The progress of data annotation technology has improved the efficiency and effect of algorithm training. As a result, computer vision methods can monitor and analyze traffic flow in real time, and the logistics and transportation industries need to track and manage vehicles in real time; and can achieve real-time positioning and tracking of vehicles, so as to achieve the purpose of optimizing transportation routes and scheduling. However, there is no technical solution in the prior art that applies computer vision methods to vehicle detection and tracking in a target area.

[0022] Based on this, a vehicle detection, tracking and scheduling method, device, electronic device and storage medium provided by an embodiment of the present invention specifically provides a method for tracking and managing vehicles in a target area by using visual recognition and re-identification technology, which specifically relates to fields such as video and image processing, object recognition, and tracking technology. By classifying and integrating the visual information obtained by the camera, it is possible to identify and track as many vehicles as possible entering the target area, record the vehicle driving trajectories, and facilitate later traceability to exclude suspicious vehicles. For example: using algorithm detection, if the camera directly captures a suspicious event or the vehicle's travel video is missing for a certain period of time, it indicates that there may be a problem with the vehicle staying in the monitoring blind area for a long time, thereby optimizing the management process of the target area.

[0023] For ease of understanding of this embodiment, first, a vehicle detection, tracking and scheduling method disclosed by an embodiment of the present invention will be introduced in detail.

[0024] Embodiment 1: An embodiment of the present invention provides a vehicle detection, tracking and scheduling method, which is applied to a target area where multiple cameras are deployed. In this embodiment, multiple cameras can be pre-deployed in the target area to ensure that the multiple cameras can cover all roads in the target area.

[0025] Based on the above description, referring to Figure 1 the flowchart of a vehicle detection, tracking and scheduling method shown, the vehicle detection, tracking and scheduling method includes the following steps: Step S102, when a vehicle enters the target area, obtain the vehicle's declaration data, image and acquisition data; wherein, the acquisition data includes: license plate information and weight data.

[0026] In this embodiment, when a vehicle enters the target area, the vehicle's declaration data, the vehicle's image and the vehicle's acquisition data can be obtained. Among them, the vehicle's acquisition data can be data collected through sensors or various detection devices, for example: license plate information and weight data.

[0027] In some embodiments, when a vehicle enters the entrance checkpoint of the target area, read the driver's IC card to obtain the vehicle's declaration data; collect the vehicle's image through a camera deployed at the entrance checkpoint; collect the vehicle's license plate information through a license plate detection device deployed at the entrance checkpoint; collect the vehicle's weight data through a weight detection device deployed at the entrance checkpoint.

[0028] When a vehicle enters the target area, it needs to wait at the entrance checkpoint. The driver swipes the IC (Integrated Circuit) card, and the vehicle's declaration data can be obtained by reading the IC card.

[0029] The camera installed at the entrance checkpoint can capture images of the vehicle while the vehicle is waiting at the entrance checkpoint. There are also license plate detection devices (e.g., license plate recognizers) and weight detection devices (e.g., weighbridges) installed at the entrance checkpoint, which can be used to collect the license plate information and weight data of the vehicle respectively.

[0030] Step S104: Input the image of the vehicle into a preset re-identification model to output the target re-identification information of the vehicle.

[0031] In this embodiment, the target re-identification information of the vehicle, that is, the ReID information, can be determined through ReID (Re-identification) technology.

[0032] ReID is a computer vision-based technology that identifies and tracks specific targets across camera scenarios through appearance features and belongs to a subtask of image retrieval. Its core goal is to solve the problem of feature matching caused by factors such as lighting differences, perspective changes, and occlusions, and it is widely used in fields such as intelligent security and smart cities.

[0033] In this embodiment, a re-identification model (i.e., ReID model) trained by collecting vehicle type data can be used to preliminarily screen the movement trajectory of the vehicle in the park. Inputting the image of the vehicle into the ReID model can output the target re-identification information of the vehicle. Among them, the re-identification information can be a string of characters.

[0034] Step S106: Bind the target re-identification information, declaration data, and acquisition data of the vehicle.

[0035] After the ReID model outputs the target re-identification information of the vehicle, this embodiment can also bind the target re-identification information, declaration data, and acquisition data of the vehicle, which is convenient for subsequent display of the target re-identification information, declaration data, and acquisition data of the vehicle together, and is also convenient for subsequent query based on the target re-identification information, declaration data, or acquisition data of the vehicle.

[0036] Step S108: When the vehicle is driving in the target area, determine multiple target images including the vehicle from the images captured by multiple cameras in the target area based on the target re-identification information.

[0037] When the vehicle is driving in the target area, it will be captured by the camera, and the images captured by the cameras in the target area may all include the vehicle. Since the ReID information of the same vehicle in different images is consistent.

[0038] Therefore, in this embodiment, target detection, target tracking, and ReID technology can be used to determine multiple target images including the vehicle from the images captured by multiple cameras in the target area based on the target re-identification information.

[0039] In some embodiments, images captured by multiple cameras in a target area can be input into a re-identification model to output re-identification information of each vehicle in each image; if there is a piece of re-identification information in the image that is the target re-identification information, then the image is a target image including the vehicle.

[0040] For example: Suppose the ReID information of a vehicle is X. In this embodiment, vehicles with ReID of X can be found in images captured by different cameras at different times, and the images including the vehicle with ReID of X are used as target images.

[0041] In addition, in this embodiment, the declared data and collected data of the previously bound vehicle can also be added to the target image.

[0042] Step S110, synthesize multiple target images into a video of the vehicle traveling in the target area.

[0043] After finding the target images, this embodiment can synthesize multiple target images into a video, and this video is the video of the vehicle traveling in the target area. Moreover, the video frames in this video are all the video frames of the vehicle traveling in the target area, without omission or missing.

[0044] An embodiment of the present invention provides a method for detecting, tracking, and scheduling vehicles. When a vehicle enters a target area, the declared data, images, and collected data of the vehicle are obtained; wherein, the collected data includes license plate information and weight data; the image of the vehicle is input into a preset re-identification model to output the target re-identification information of the vehicle; the target re-identification information, declared data, and collected data of the vehicle are bound; when the vehicle travels in the target area, based on the target re-identification information, multiple target images including the vehicle are determined from the images captured by multiple cameras in the target area; multiple target images are synthesized into a video of the vehicle traveling in the target area. In this way, by classifying and integrating the visual information obtained by the camera, as many vehicles entering the target area as possible can be identified and tracked, thereby optimizing the management process of the target area.

[0045] Embodiment Two: This embodiment provides another method for detecting, tracking, and scheduling vehicles. This method is implemented on the basis of the above embodiment, and focuses on describing the specific manner of generating the driving trajectory of the vehicle in the target area. Refer to Figure 2 the flowchart of another method for detecting, tracking, and scheduling vehicles shown. This method for detecting, tracking, and scheduling vehicles includes the following steps: Step S202, when a vehicle enters a target area, obtain the declared data, images, and collected data of the vehicle; wherein, the collected data includes license plate information and weight data.

[0046] Step S204: Input the image of the vehicle into a preset re-identification model to output the target re-identification information of the vehicle.

[0047] Step S206: Bind the target re-identification information of the vehicle, the declaration data, and the acquisition data.

[0048] Step S208: When the vehicle is traveling in the target area, determine multiple target images including the vehicle from the images captured by multiple cameras in the target area based on the target re-identification information.

[0049] Step S210: Synthesize the multiple target images into a video of the vehicle traveling in the target area.

[0050] Step S212: Determine the driving trajectory of the vehicle in the target area based on the video.

[0051] In this embodiment, object detection and object tracking technologies can be utilized to generate the driving trajectory of the vehicle in the target area based on the video of the vehicle traveling in the target area.

[0052] Object Detection is a core technology in the field of computer vision, aiming to identify and locate specific target objects from images or videos and output their class labels and location information (usually represented by bounding boxes).

[0053] Object Tracking is a key technology in the field of computer vision, aiming to continuously locate and predict the trajectory of a specific target in a video sequence and output information such as its motion path, pose changes, and bounding box coordinates.

[0054] In some embodiments, the video can be input into a preset object detection model to output the bounding box of the vehicle; the vehicle in the video can be determined based on the bounding box of the vehicle; and the driving trajectory of the vehicle in the target area can be determined based on the vehicle in the video through object tracking.

[0055] In this embodiment, deep learning algorithms can be used to perform object detection on the vehicle in the video of the vehicle traveling in the target area. Among them, the above deep learning algorithms can be: YOLO (You Only Look Once, a kind of object detection model) algorithm, SSD (Single Shot MultiBox Detector) algorithm, Faster R-CNN algorithm, etc. Using the above deep learning algorithms, the vehicle in the video frame can be identified and located in real time, and the bounding box of the vehicle can be generated.

[0056] Based on the results of object detection, the motion trajectory of the vehicle can be tracked using an object tracking algorithm. Among them, the above object tracking algorithm can be: SORT (Simple Online and Realtime Tracking) algorithm, DeepSORT (Deep Simple Online and Realtime Tracking) algorithm, etc. By using the above object tracking algorithm, the continuous tracking of the vehicle can be realized by associating the objects in different video frames.

[0057] In some embodiments, it is also possible to respond to the user's operation of clicking on the vehicle in the video and display the target re-identification information, declaration data, and acquisition data of the vehicle.

[0058] In this embodiment, the target re-identification information, declaration data, and acquisition data of the vehicle are pre-bound. Therefore, when displaying the video of the vehicle driving in the target area, if the user clicks on the vehicle, the target re-identification information, declaration data, and acquisition data of the vehicle can be displayed simultaneously to provide the user with the most comprehensive information about the vehicle and facilitate the user to intuitively and comprehensively understand the situation of the vehicle.

[0059] In some embodiments, it is also possible to obtain a query instruction and determine the video corresponding to the query instruction; among them, the query instruction includes: the re-identification information of the vehicle or the license plate information.

[0060] In this embodiment, since the target re-identification information, declaration data, and acquisition data of the vehicle are pre-bound, the user can query the video of the vehicle driving in the target area through the re-identification information or license plate information of the vehicle, so as to perform video playback. In addition, this embodiment can also play back multiple segments separately, and this embodiment does not limit this.

[0061] In summary, the above method provided by the embodiments of the present invention can use a multi-camera system to finely fit the action trajectory screened by the ReID model by fusing the image data from multiple perspectives and the relative positions of the cameras, so as to obtain a relatively accurate vehicle global trajectory and retain relevant video segments for other needs.

[0062] Embodiment 3: Corresponding to the above method embodiment, the embodiment of the present invention provides a vehicle detection, tracking and scheduling device, see Figure 3 the structural schematic diagram of a vehicle detection, tracking and scheduling device shown. The vehicle detection, tracking and scheduling device includes: A data acquisition and collection module 31, configured to acquire the declaration data, images, and acquisition data of the vehicle when the vehicle enters the target area; among them, the acquisition data includes: license plate information and weight data; The re-identification module 32 is configured to input an image of a vehicle into a preset re-identification model and output target re-identification information of the vehicle; The information and data binding module 33 is configured to bind the target re-identification information, declaration data, and acquisition data of the vehicle; The target image determination module 34 is configured to, when the vehicle is traveling in a target area, determine multiple target images including the vehicle from the images captured by multiple cameras in the target area based on the target re-identification information; The video synthesis module 35 is configured to synthesize the multiple target images into a video of the vehicle traveling in the target area.

[0063] An embodiment of the present invention provides a detection, tracking, and scheduling device for a vehicle, which acquires declaration data, images, and acquisition data of the vehicle when the vehicle enters a target area; wherein, the acquisition data includes license plate information and weight data; inputs the image of the vehicle into a preset re-identification model and outputs target re-identification information of the vehicle; binds the target re-identification information, declaration data, and acquisition data of the vehicle; when the vehicle is traveling in the target area, determines multiple target images including the vehicle from the images captured by multiple cameras in the target area based on the target re-identification information; synthesizes the multiple target images into a video of the vehicle traveling in the target area. In this way, the visual information obtained by the camera can be classified and integrated, so as to identify and track as many vehicles entering the target area as possible, thereby optimizing the management process of the target area.

[0064] The above data acquisition and collection module is configured to read the driver's IC card to obtain the vehicle's declaration data when the vehicle enters the entrance checkpoint of the target area; collect the vehicle's image through a camera arranged at the entrance checkpoint; collect the vehicle's license plate information through a license plate detection device arranged at the entrance checkpoint; collect the vehicle's weight data through a weight detection device arranged at the entrance checkpoint.

[0065] The above target image determination module is configured to input the images captured by multiple cameras in the target area into the re-identification model and output the re-identification information of each vehicle in each image; if there is a re-identification information in the image that is the target re-identification information, then the image is a target image including the vehicle.

[0066] The above device further includes: a driving trajectory generation module, configured to determine the driving trajectory of the vehicle in the target area based on the video.

[0067] The above driving trajectory generation module is configured to input the video into a preset target detection model and output the bounding box of the vehicle; determine the vehicle in the video based on the bounding box of the vehicle; determine the driving trajectory of the vehicle in the target area by means of target tracking based on the vehicle in the video.

[0068] The above device further includes: an information and data display module, which is used to respond to the operation of the user clicking on the vehicle in the video, and display the target re-identification information, declared data, and collected data of the vehicle.

[0069] The above device further includes: a video query module, which is used to obtain a query instruction and determine the video corresponding to the query instruction; wherein, the query instruction includes: the re-identification information or license plate information of the vehicle.

[0070] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described vehicle detection, tracking, and scheduling device can refer to the corresponding process in the embodiment of the foregoing vehicle detection, tracking, and scheduling method, which will not be elaborated herein.

[0071] Embodiment 4: The embodiment of the present invention further provides an electronic device for running the above vehicle detection, tracking, and scheduling method; see Figure 4 the structural schematic diagram of an electronic device shown. The electronic device includes a memory 100 and a processor 101. Among them, the memory 100 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 101 to implement the above vehicle detection, tracking, and scheduling method.

[0072] Furthermore, Figure 4 the electronic device shown further includes a bus 102 and a communication interface 103, and the processor 101, the communication interface 103, and the memory 100 are connected through the bus 102.

[0073] Among them, the memory 100 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 103 (which can be wired or wireless), a communication connection between the system network element and at least one other network element can be realized, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 102 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 4 only a bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0074] The processor 101 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 101 or the instructions in the form of software. The above-mentioned processor 101 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 100, and the processor 101 reads the information in the memory 100 and combines its hardware to complete the steps of the method in the foregoing embodiments.

[0075] The embodiments of the present invention also provide a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions cause the processor to implement the above-mentioned vehicle detection, tracking, and scheduling method. For the specific implementation, reference can be made to the method embodiments, and details are not described herein again.

[0076] The computer program product of the vehicle detection, tracking, and scheduling method, device, electronic device, and storage medium provided by the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the foregoing method embodiments. For the specific implementation, reference can be made to the method embodiments, and details are not described herein again.

[0077] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system and / or device can refer to the corresponding processes in the foregoing method embodiments, and details are not described herein again.

[0078] In addition, in the description of the embodiments of the present invention, unless otherwise clearly defined and limited, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0079] If a function 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 such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0080] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0081] Finally, it should be noted that the above embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A vehicle detection, tracking and scheduling method, characterized in that: Applied to a target area, where a plurality of cameras are arranged, the method comprises: When a vehicle enters the target area, the declared data, image and collected data of the vehicle are obtained; wherein the collected data includes: license plate information and weight data; Inputting the image of the vehicle into a preset re-identification model, and outputting target re-identification information of the vehicle; Binding the target re-identification information of the vehicle, the declared data and the collected data; When the vehicle is traveling in the target area, determining a plurality of target images including the vehicle from images captured by a plurality of cameras in the target area based on the target re-identification information; The plurality of target images are synthesized into a video of the vehicle driving in the target area.

2. The method according to claim 1, characterized in that The step of obtaining the declared data, image and collected data of the vehicle when the vehicle enters the target area comprises: When a vehicle enters the entrance checkpoint of the target area, the driver's IC card is read to obtain the declared data of the vehicle; Collecting an image of the vehicle by using a camera arranged at the entrance checkpoint; Collecting the license plate information of the vehicle through a license plate detection device arranged at the entrance checkpoint; The weight data of the vehicle is collected by a weight detection device arranged at the entrance checkpoint.

3. The method according to claim 1, characterized in that The step of determining a plurality of target images including the vehicle from images captured by a plurality of cameras in the target area based on the target re-identification information comprises: Inputting images captured by multiple cameras in the target area into the re-identification model, and outputting re-identification information of each of the vehicles in each image; If there is re-identification information in the image that is the target re-identification information, then the image is a target image including the vehicle.

4. The method according to claim 1, characterized in that: After the step of synthesizing the plurality of target images into a video of the vehicle traveling in the target area, the method further comprises: Determine the driving trajectory of the vehicle in the target area based on the video.

5. The method according to claim 4, characterized in that The step of determining the driving trajectory of the vehicle in the target area based on the video includes: Inputting the video into a preset target detection model and outputting a bounding box of the vehicle; determining the vehicle in the video based on a bounding box of the vehicle; The driving trajectory of the vehicle in the target area is determined based on the vehicle in the video by target tracking.

6. The method according to any one of claims 1 to 5, characterized in that: After the step of synthesizing the plurality of target images into a video of the vehicle traveling in the target area, the method further comprises: In response to a user clicking on the vehicle in the video, target re-identification information, the declared data and the collected data of the vehicle are displayed.

7. The method according to any one of claims 1 to 5, characterized in that: After the step of synthesizing the plurality of target images into a video of the vehicle traveling in the target area, the method further comprises: Obtain a query instruction and determine a video corresponding to the query instruction; wherein the query instruction includes: vehicle re-identification information or license plate information.

8. A vehicle detection, tracking and dispatching device, characterized in that: Applied to a target area, where a plurality of cameras are arranged, the device comprises: A data acquisition and collection module, used to acquire the declared data, image and collected data of the vehicle when the vehicle enters the target area; wherein the collected data includes: license plate information and weight data; A re-identification module, used for inputting the image of the vehicle into a preset re-identification model and outputting target re-identification information of the vehicle; An information and data binding module, used to bind the target re-identification information of the vehicle, the declared data and the collected data; a target image determination module, configured to determine, when the vehicle is traveling in the target area, a plurality of target images including the vehicle from images captured by a plurality of cameras in the target area based on the target re-identification information; The video synthesis module is used to synthesize the multiple target images into a video of the vehicle driving in the target area.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the vehicle detection, tracking and scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the vehicle detection, tracking and scheduling method according to any one of claims 1 to 7.

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