Target detection method, system and device based on 3D video and medium

Abstract

The invention discloses a target detection method, system and device based on a 3D video and a medium, and relates to the technical field of image processing, and the method comprises the steps: determining a 3D detection frame corresponding to a target detection object in a to-be-detected region according to any frame of image data of the to-be-detected region; and longitude and latitude coordinates of a reference vertex corresponding to any target detection object are determined based on the 3D detection frame, basic attribute information of the target detection object is determined by combining the longitude and latitude coordinates of acquisition equipment, and the basic attribute information comprises height information of the target detection object. According to the scheme, in the process of determining the basic attribute information of the target detection object, the latitude and longitude coordinates of the acquisition equipment are combined, so that the determination result of the basic attribute information of the target detection object is more fit with the real situation, and the data is more credible.

Description

Background Art

[0002] With the development of the intelligent network "vehicle-road-cloud integration" urban pilot, traditional millimeter-wave radars and cameras, as the main road test hardware, have the advantage of relatively low cost and are widely used. Under normal circumstances, millimeter-wave radars cannot detect the specific length, width and height scale information of the target, and 2D video target detection algorithms cannot obtain the scale information of the target. This description proposes a method based on 3D video target detection. After a certain algorithm calculation, the length, width and height size information of the vehicle can be accurately obtained in real time. In addition, the pixel coordinates after 3D target detection and the geographic location coordinate information after algorithm mapping are more accurate than the target geographic information obtained by millimeter-wave radar algorithms and 2D video target detection algorithms. It is then used in digital twin display or reported to the cloud service end as traffic event information, which is convenient for the terminal or monitoring platform to obtain accurate dynamic information of the vehicle in real time, providing safer protection for road traffic and promoting the development of autonomous driving technology.

[0003] By detecting the target in the video screen, the size information of the vehicle is obtained. The existing technology usually takes the meter coordinates of the geodetic coordinates as the benchmark, measures the meter coordinates of the ground marking lines, and associates the pixel points on the edge of the vehicle with the meter coordinate points of the marking lines to calculate the length and width information of the vehicle target. It is more difficult to obtain the height information of the vehicle. In addition, when calculating the geographic location information of the vehicle target, it is usually based on the central pixel coordinates of the 2D target detection frame or the bottom center pixel coordinates as the benchmark. There is a certain deviation between the geographic coordinates mapped by the algorithm and the actual geographic coordinates of the vehicle, especially when a truck is detected, the deviation is relatively large. Summary of the invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies of the prior art and specifically provide a 3D video-based target detection method, system, device and medium, as follows:

[0005] 1) In the first aspect, the present invention provides a target detection method based on 3D video, and the specific technical solution is as follows:

[0006] Determine a 3D detection frame corresponding to the target detection object in the area to be detected according to any frame image data of the area to be detected;

[0007] The latitude and longitude coordinates of the reference vertex corresponding to any target detection object are determined based on the 3D detection frame, and the basic attribute information of the target detection object is determined in combination with the latitude and longitude coordinates of the acquisition device, wherein the basic attribute information includes the height information of the target detection object.

[0008] The beneficial effects of a 3D video-based object detection method provided by the present invention are as follows:

[0009] This solution uses a 3D detection frame to determine the longitude and latitude of subsequent benchmark points, which is more accurate than the 2D detection frame method and can reflect the characteristics of the target detection object in more detail. In addition, this solution combines the longitude and latitude coordinates of the acquisition device in the process of determining the basic attribute information of the target detection object, so that the determination results of the basic attribute information of the target detection object are more consistent with the actual situation and the data is more credible. In addition, the basic attribute information also includes height information, which solves the problem that the current video target detection cannot obtain the actual height.

[0010] Based on the above solution, the present invention can also be improved as follows.

[0011] Further, the process of determining the latitude and longitude coordinates of the reference vertex corresponding to any target detection object is:

[0012] Determine the pixel coordinates of a reference vertex corresponding to any target detection object based on the 3D detection frame;

[0013] According to the coordinate conversion principle, the pixel coordinates of any reference vertex are converted into longitude and latitude coordinates.

[0014] Furthermore, the process of determining the basic attribute information of the target detection object is:

[0015] Based on the correspondence between the longitude and latitude coordinates of all reference vertices, the longitude and latitude coordinates of the acquisition device, and the center coordinates of the target detection object, determine the target center coordinates corresponding to the longitude and latitude coordinates of the reference vertices and the longitude and latitude coordinates of the acquisition device, and determine the basic attribute information based on the target center coordinates.

[0016] Furthermore, it also includes:

[0017] The basic attribute information is sent to the cloud service end for storage.

[0018] 2) In a second aspect, the present invention further provides a target detection system based on 3D video, and the specific technical solution is as follows:

[0019] Determine a 3D detection frame corresponding to the target detection object in the area to be detected according to any frame image data of the area to be detected;

[0020] The latitude and longitude coordinates of the reference vertex corresponding to any target detection object are determined based on the 3D detection frame, and the basic attribute information of the target detection object is determined in combination with the latitude and longitude coordinates of the acquisition device, wherein the basic attribute information includes the height information of the target detection object.

[0021] Based on the above solution, the present invention can also be improved as follows.

[0022] Further, the process of determining the latitude and longitude coordinates of the reference vertex corresponding to any target detection object is:

[0023] Determine the pixel coordinates of a reference vertex corresponding to any target detection object based on the 3D detection frame;

[0024] According to the coordinate conversion principle, the pixel coordinates of any reference vertex are converted into longitude and latitude coordinates.

[0025] Furthermore, the process of determining the basic attribute information of the target detection object is:

[0026] Based on the correspondence between the longitude and latitude coordinates of all reference vertices, the longitude and latitude coordinates of the acquisition device, and the center coordinates of the target detection object, determine the target center coordinates corresponding to the longitude and latitude coordinates of the reference vertices and the longitude and latitude coordinates of the acquisition device, and determine the basic attribute information based on the target center coordinates.

[0027] Furthermore, it also includes:

[0028] The basic attribute information is sent to the cloud service end for storage.

[0029] 3) In a third aspect, the present invention further provides an electronic device, comprising a processor, wherein the processor is coupled to a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements any of the above methods.

[0030] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor so that a computer implements any of the above methods.

[0031] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0033] Figure 1 This is a flow chart of a method for object detection based on 3D video according to an embodiment of the present invention;

[0034] Figure 2 The second flowchart of a method for object detection based on 3D video according to an embodiment of the present invention;

[0035] Figure 3 This is a structural framework diagram of an electronic device in this scheme;

[0036] Figure 4 A schematic diagram of reference vertices of a 3D video-based object detection method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0038] like Figure 1 as well as Figure 2 As shown, a target detection method based on 3D video according to an embodiment of the present invention comprises the following steps:

[0039] S1, determining a 3D detection frame corresponding to a target detection object in the area to be detected according to any frame image data of the area to be detected;

[0040] S2, based on the 3D detection frame, determine the longitude and latitude coordinates of the reference vertex corresponding to any target detection object, and determine the basic attribute information of the target detection object in combination with the longitude and latitude coordinates of the acquisition device, wherein the basic attribute information includes the height information of the target detection object.

[0041] The beneficial effects of a 3D video-based object detection method provided by the present invention are as follows:

[0042] This solution uses a 3D detection frame to determine the longitude and latitude of subsequent benchmark points, which is more accurate than the 2D detection frame method and can reflect the characteristics of the target detection object in more detail. In addition, this solution combines the longitude and latitude coordinates of the acquisition device in the process of determining the basic attribute information of the target detection object, so that the determination results of the basic attribute information of the target detection object are more consistent with the actual situation and the data is more credible. In addition, the basic attribute information also includes height information, which solves the problem that the current video target detection cannot obtain the actual height.

[0043] S1, determining a 3D detection frame corresponding to a target detection object in the area to be detected based on any frame image data of the area to be detected.

[0044] The area to be detected refers to the road area that can be collected by the collection equipment and allows vehicles and pedestrians to pass normally. Depending on the location of the collection equipment, the corresponding location of the area to be detected is also different, including but not limited to: intersections, highway intersections, etc.

[0045] The image data is obtained as follows:

[0046] By decoding the video stream captured by the acquisition device, each frame of image data is obtained. The decoding process includes:

[0047] The decoder decodes the compressed video stream that complies with a specific coding standard to obtain image data.

[0048] The process of determining the 3D detection frame corresponding to the target detection object according to the image data is as follows: wherein the target detection object is a vehicle.

[0049] By presetting the vehicle detection model, the image data is processed to obtain the initial 3D detection frame of all target detection objects corresponding to any image data;

[0050] All initial 3D detection frames on any image data are rechecked, and the initial 3D detection frames corresponding to non-motor vehicles are first eliminated, and then the initial 3D detection frames with obvious detection errors are adjusted to obtain the 3D detection frames.

[0051] An initial 3D detection frame with obvious detection errors refers to: determining the actual length of the vehicle and comparing it with the corresponding detection edge length in the initial 3D detection frame. If the difference between the detection edge length and the actual length exceeds the error range, the initial 3D detection frame corresponding to the detection edge is determined as the initial 3D detection frame with obvious detection errors. At this time, feature extraction is performed on the area corresponding to the initial 3D detection frame to obtain an image containing only the area corresponding to the initial 3D detection frame, or partial highlighting is performed on the image data corresponding to the initial 3D detection frame to obtain a new image, so that the features of the initial 3D detection frame with obvious detection errors are more prominent. The processed image data is re-input into the preset vehicle detection model for detection, and the results are verified again. If there are still initial 3D detection frames with obvious detection errors, the 3D detection frame is re-determined through manual intervention.

[0052] The preset vehicle detection model is trained in the following way:

[0053] After constructing the training sample set, the sample images in the training sample set are input into the vehicle detection model, and are passed through the input layer to the hidden layer for feature extraction. The sample images are reduced in dimension through the pooling layer, and the reduced sample images are input into the three-layer convolution layer for vehicle feature extraction. The extracted results are integrated and classified through the fully connected layer and passed to the output layer for output. The entire classification result is compared with the label category corresponding to each image in the validation set, and combined with the loss function, it is determined whether the current vehicle detection model reaches the preset accuracy. If it reaches the preset accuracy, the vehicle model is output.

[0054] During the training process, a feature extraction layer can be added after any convolution layer. The feature extraction layer is not connected to the fully connected layer. The feature extraction layer directly extracts the features of the image processed by a certain convolution layer according to preset rules, and analyzes the extracted features (the analysis process can be performed manually or by comparing features with labels, such as performing semantic analysis on labels, extracting keywords, and determining the similarity between features and keywords. When the similarity is higher than the preset similarity, it is determined that the requirements are met or that the feature extraction is correct), and whether the processing of the current convolution layer is accurate. If the processing result is inaccurate, the convolution layer of the model is replaced or adjusted to make the convolution layer more accurate in extracting features.

[0055] S2, based on the 3D detection frame, determine the latitude and longitude coordinates of the reference vertex corresponding to any target detection object, and determine the basic attribute information of the target detection object in combination with the latitude and longitude coordinates of the acquisition device, wherein the basic attribute information includes the height information of the target detection object.

[0056] There are four reference vertices, specifically: taking the central axis in the two-dimensional image corresponding to any frame of image data (the axis parallel to the boundary line of the two-dimensional image that divides the entire two-dimensional image into two parts along the shooting direction of the acquisition device) as the standard, the two-dimensional image is divided into a left half and a right half. In the two-dimensional image of the left half, the 3D detection frame corresponding to any target detection object is composed of six two-dimensional detection frames, wherein the six two-dimensional detection frames are three groups of detection frames that are parallel to each other. The two-dimensional detection frame that is parallel to the ground and closer to the ground is defined as the reference two-dimensional detection frame. In the reference two-dimensional detection frame, the vertex with the smallest distance from the acquisition device among the two vertices that are closer to the central axis is determined, and the vertex is defined as the reference point. The reference point and the two vertices adjacent to the reference point and the vertex that is perpendicular to the reference point in the two-dimensional detection frame that is parallel to the ground and farther from the ground (also called the target point) are defined as the reference vertex. Wherein, the vertical correspondence is that the line connecting the target point and the reference point is perpendicular to the ground.

[0057] For the convenience of subsequent explanation, Figure 4As shown, the four reference vertices are represented by a, b, c and d, where d is the target point and b is the reference point. f1 is the installation position of the acquisition device, and f2 is the point corresponding to the vertical ground of the acquisition device. The line connecting f1 and point d in the figure is line 1, and the line connecting f2 and point b is line 2. When the vertical error is small, line 1 and line 2 should be approximately compared to the ground point e. The installation location of the acquisition device is Pos0 (lon0, lat0), and the installation height from the ground is h meters (that is, the side length of f1f2 is h); the pixels in each frame of the image are mapped by the algorithm to obtain their corresponding geographic longitude and latitude coordinates. The geographic longitude and latitude coordinates of the reference vertices a, b, c, d, and e are: reference vertex a: Posa (aLon, aLot), reference vertex b: Posb (bLon, bLot), reference vertex c: Posc (cLon, cLot), reference vertex d: Posd (dLon, dLot), reference vertex e: Pose (eLon, eLot). The center coordinates of the target detection object are centerPos (centerLon, centerLot), then centerLon = (aLon + cLon) / 2; centerLat = (aLat + cLat) / 2. The center point coordinates are the midpoint of the line between the two vertices adjacent to the reference point.

[0058] Since the pixel coordinates and longitude and latitude algorithm mapping in this description are based on the geodetic plane, from the perspective of the camera (acquisition device), point d is projected to point e on the ground through the camera, and the pixel coordinates of point d reach point e on the geodetic plane after algorithm mapping, and then dLon==eLon, dLot==eLot; the distance(posA,posB) function is a function for calculating the distance in meters between the longitude and latitude of two points, based on the parallel line property theorem of one side of a triangle.

[0059] The height of the target high = h × distance (Pose, Posb) / distance (Pose, Pos0). After taking the average of the height of multiple frames, the obtained height is more accurate.

[0060] By obtaining the precise location and size information of the target, the judgment logic of traffic events is simplified. For example, events such as trucks exceeding the height limit, trucks not driving in the designated lanes, and motor vehicles changing lanes continuously can greatly improve the accuracy of event reporting and solve the defects of 2D target detection.

[0061] The algorithm sends the geographic coordinate information and size information of the vehicle target, as well as the event messages after logical processing, to the cloud server according to the protocol. The terminal or monitoring platform accurately displays the twin in real time, providing safer protection for pedestrians or vehicles on surrounding roads. At the same time, it can make predictions in advance and plan and issue reasonable driving routes for autonomous driving vehicles.

[0062] Further, the process of determining the latitude and longitude coordinates of the reference vertex corresponding to any target detection object is:

[0063] Determine the pixel coordinates of a reference vertex corresponding to any target detection object based on the 3D detection frame;

[0064] According to the coordinate conversion principle, the pixel coordinates of any reference vertex are converted into longitude and latitude coordinates.

[0065] Furthermore, the process of determining the basic attribute information of the target detection object is:

[0066] Based on the correspondence between the longitude and latitude coordinates of all reference vertices, the longitude and latitude coordinates of the acquisition device, and the center coordinates of the target detection object, determine the target center coordinates corresponding to the longitude and latitude coordinates of the reference vertices and the longitude and latitude coordinates of the acquisition device, and determine the basic attribute information based on the target center coordinates.

[0067] Furthermore, it also includes:

[0068] The basic attribute information is sent to the cloud service end for storage.

[0069] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0070] The border of 3D target detection represents the edge information of the target vehicle in more detail than that of 2D target detection. This description proposes a method that, after a simple 5-point calibration configuration, uses image pixels and geographic coordinate mapping to obtain the precise latitude and longitude position information and length, width and height size information of the target. The obtained central longitude and latitude coordinates are more accurate than the coordinates calculated by the millimeter wave radar algorithm and the 2D video target detection algorithm. In particular, the method for obtaining height information solves the problem that the current video target detection cannot obtain the actual height of the target vehicle. When sent to the terminal or monitoring platform, the effect of 1:1 display of real-time traffic targets is achieved. In addition, since the bottom border of the 3D target detection basically has no height information on the ground, it is more convenient to determine the driving trajectory of the vehicle. These characteristics are used to simplify the judgment logic of traffic events, such as trucks exceeding the height limit, trucks not driving in the specified lanes, and motor vehicles changing lanes continuously, which can greatly improve the accuracy of event reporting; It provides safer protection for pedestrians or vehicles on surrounding roads, and can make early predictions to plan and issue reasonable driving routes for autonomous driving vehicles.

[0071] The present invention also provides a target detection system based on 3D video, and the specific technical solution is as follows:

[0072] Determine a 3D detection frame corresponding to the target detection object in the area to be detected according to any frame image data of the area to be detected;

[0073] The latitude and longitude coordinates of the reference vertex corresponding to any target detection object are determined based on the 3D detection frame, and the basic attribute information of the target detection object is determined in combination with the latitude and longitude coordinates of the acquisition device, wherein the basic attribute information includes the height information of the target detection object.

[0074] Based on the above solution, the present invention can also be improved as follows.

[0075] Further, the process of determining the latitude and longitude coordinates of the reference vertex corresponding to any target detection object is:

[0076] Determine the pixel coordinates of a reference vertex corresponding to any target detection object based on the 3D detection frame;

[0077] According to the coordinate conversion principle, the pixel coordinates of any reference vertex are converted into longitude and latitude coordinates.

[0078] Furthermore, the process of determining the basic attribute information of the target detection object is:

[0079] Based on the correspondence between the longitude and latitude coordinates of all reference vertices, the longitude and latitude coordinates of the acquisition device, and the center coordinates of the target detection object, determine the target center coordinates corresponding to the longitude and latitude coordinates of the reference vertices and the longitude and latitude coordinates of the acquisition device, and determine the basic attribute information based on the target center coordinates.

[0080] Furthermore, it also includes:

[0081] The basic attribute information is sent to the cloud service end for storage.

[0082] It should be noted that the beneficial effects of the target detection system based on 3D video provided by the above embodiment are the same as the beneficial effects of the target detection method based on 3D video, which will not be described in detail here. In addition, when the system provided by the above embodiment realizes its functions, it only uses the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided by the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be described in detail here.

[0083] like Figure 3As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above methods, specifically:

[0084] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the electronic device 300 implements a target detection method based on 3D video provided in the above embodiment. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the electronic device 300 may also include other components for implementing device functions, which will not be repeated here.

[0085] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements any one of the above methods.

[0086] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0087] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs any of the above methods.

[0088] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0089] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0090] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0091] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A target detection method based on 3D video, characterized in that: include: Determine a 3D detection frame corresponding to the target detection object in the area to be detected according to any frame image data of the area to be detected; The latitude and longitude coordinates of the reference vertex corresponding to any target detection object are determined based on the 3D detection frame, and the basic attribute information of the target detection object is determined in combination with the latitude and longitude coordinates of the acquisition device, wherein the basic attribute information includes the height information of the target detection object.

2. The object detection method based on 3D video according to claim 1, characterized in that: The process of determining the latitude and longitude coordinates of the reference vertex corresponding to any target detection object is as follows: Determine the pixel coordinates of a reference vertex corresponding to any target detection object based on the 3D detection frame; According to the coordinate conversion principle, the pixel coordinates of any reference vertex are converted into longitude and latitude coordinates.

3. The object detection method based on 3D video according to claim 1, characterized in that: The process of determining the basic attribute information of the target detection object is: Based on the correspondence between the longitude and latitude coordinates of all reference vertices, the longitude and latitude coordinates of the acquisition device, and the center coordinates of the target detection object, determine the target center coordinates corresponding to the longitude and latitude coordinates of the reference vertices and the longitude and latitude coordinates of the acquisition device, and determine the basic attribute information based on the target center coordinates.

4. The object detection method based on 3D video according to claim 1, characterized in that: Also includes: The basic attribute information is sent to the cloud service end for storage.

5. A 3D video-based target detection system, characterized in that: include: Determine a 3D detection frame corresponding to the target detection object in the area to be detected according to any frame image data of the area to be detected; The latitude and longitude coordinates of the reference vertex corresponding to any target detection object are determined based on the 3D detection frame, and the basic attribute information of the target detection object is determined in combination with the latitude and longitude coordinates of the acquisition device, wherein the basic attribute information includes the height information of the target detection object.

6. The object detection system based on 3D video according to claim 5, characterized in that: The process of determining the latitude and longitude coordinates of the reference vertex corresponding to any target detection object is as follows: Determine the pixel coordinates of a reference vertex corresponding to any target detection object based on the 3D detection frame; According to the coordinate conversion principle, the pixel coordinates of any reference vertex are converted into longitude and latitude coordinates.

7. The object detection system based on 3D video according to claim 5, characterized in that: The process of determining the basic attribute information of the target detection object is: Based on the correspondence between the longitude and latitude coordinates of all reference vertices, the longitude and latitude coordinates of the acquisition device, and the center coordinates of the target detection object, determine the target center coordinates corresponding to the longitude and latitude coordinates of the reference vertices and the longitude and latitude coordinates of the acquisition device, and determine the basic attribute information based on the target center coordinates.

8. The 3D video-based target detection system according to claim 5, characterized in that: Also includes: The basic attribute information is sent to the cloud service end for storage.

9. An electronic device, characterized in that: The electronic device comprises a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements the method according to any one of claims 1 to 4.

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