Simulation test method and simulation test system for visual algorithm

Abstract

The present application discloses a simulation test method and a simulation test system for a visual algorithm, wherein the simulation test method includes obtaining a captured real-time video, analyzing the real-time video through a target detection algorithm to obtain a target frame detection log for each frame of the real-time video; analyzing the target frame detection log through a tracking algorithm to obtain a tracking log for each frame of the real-time video; writing the target frame detection log and the tracking log into the real-time video to obtain a first video for simulation testing of the tracking algorithm. The simulation test system includes an automatic tracking shooting device and a simulation device, which are suitable for obtaining the target frame detection log and the tracking log and writing them into the real-time video to obtain a first video for simulation testing. Through the testing method of the present application, the tester can clearly see the tracking point situation obtained by the tracking algorithm, thereby locating the cause of the tracking anomaly. At the same time, it is also convenient for the tester to intuitively compare the tracking effects of different tracking algorithms.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of testing computer vision algorithms, and in particular to simulation testing of motion tracking algorithms and zoom algorithms. Background Art

[0002] Tracking filming in sports is a relatively new application scenario. Its goal is to track and capture the focal point of an entire match using an independent, rotatable camera. Motion tracking algorithms identify and analyze key targets on the field (people or the ball, and potentially also field elements such as field lines, goal nets, and corner flags) to determine the movement of the core focal point. Using a rotating mechanism, the camera adjusts the camera angle to track the action, mimicking a real-life tracking shot. However, if the tracking algorithm fails, the camera may rotate incorrectly or freeze, resulting in an inability to accurately capture the focal point of the match. With existing technologies, users or producers can only detect tracking issues by viewing the captured footage, but cannot pinpoint the cause. For example, tracking errors may be caused by failed target recognition, interference in the video, or a mismatch in the algorithm's logical priorities. However, the captured video only reveals the tracking results, but cannot accurately determine the cause. Therefore, there is an urgent need for a technical solution that can intuitively display the computational process of tracking filming algorithms. Summary of the Invention

[0003] In light of this, this application proposes a simulation testing method. By defining and exporting the log format of the algorithm calculation intermediate process and writing the log content to the real-time video, users or developers can intuitively analyze the tracking process through the real-time video to accurately determine the cause of tracking errors. At the same time, based on the original video and algorithm log, the zoom algorithm can be easily and repeatedly tested.

[0004] To achieve the above objectives, this application adopts the following technical solutions:

[0005] In a first aspect of the present application, a simulation test method for a visual tracking algorithm is provided, comprising:

[0006] Get the collected real-time video;

[0007] Analyze the real-time video through the target detection algorithm to obtain the target frame detection log of each frame of the real-time video;

[0008] Analyzing the target frame detection log using a tracking algorithm to obtain a tracking log for each frame of the real-time video, the tracking log including the tracking point position, wherein the tracking algorithm includes a first algorithm to be tested;

[0009] The target frame detection log and tracking log are written into the real-time video to obtain the first video for the tracking algorithm simulation test.

[0010] In one embodiment of the present application, the target frame detection log includes the target type, position, and size of each target frame.

[0011] In another embodiment of the present application, the tracking log also includes the displacement speed of one or more target frames specified by a preset strategy of the tracking algorithm.

[0012] The one or more target frames specified by the preset strategy of the tracking algorithm may include: a preset number of human target frames and / or ball target frames with the fastest speed.

[0013] The tracking algorithm may further include a second algorithm to be tested, and the method may further include writing a second tracking log obtained by executing the S3 step based on the second algorithm to be tested into the first video to obtain a second video for comparative testing of the tracking algorithm.

[0014] Preferably, the simulation test method further includes:

[0015] The target frame detection log and tracking point position are analyzed by the zoom algorithm to be tested, and the zoom control result is written into the real-time video to obtain a third video for zoom algorithm simulation test.

[0016] In a second aspect of the present application, an automatic tracking and shooting device is provided, including a shooting module, an analysis module and a rotation module, wherein the shooting module is physically connected to the rotation module, the rotation module is suitable for horizontal rotation, the shooting module is suitable for continuously capturing real-time video, and sending the real-time video frame by frame to the analysis module; the analysis module is suitable for loading a target detection algorithm and a tracking algorithm to track and shoot the game, generate a rotation instruction, and send the rotation instruction to the rotation module; the analysis module is also suitable for outputting a target frame detection log of the target detection algorithm and a tracking log of the tracking algorithm; the rotation module is suitable for changing the angle of the shooting module according to the rotation instruction.

[0017] In a third aspect of the present application, a simulation test system is provided, characterized by comprising:

[0018] As in the automatic tracking shooting device and simulation device of the second aspect of the present application, the simulation device is communicatively connected to the automatic tracking shooting device, and is suitable for obtaining the target frame detection log and the tracking log and writing them into real-time video to obtain a first video for simulation testing.

[0019] Preferably, the simulation module is further adapted to analyze the target frame detection log and the tracking point position through the zoom algorithm to be tested, write the zoom control result into the real-time video, and obtain a third video for zoom algorithm simulation testing.

[0020] In a fourth aspect of the present application, an electronic device is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method of the first aspect of the present application is implemented.

[0021] In a fifth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method of the first aspect of the present application is implemented.

[0022] In a sixth aspect of the present application, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, it implements the method of the first aspect of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0024] Figure 1 Flowchart of a tracking algorithm simulation test method according to an embodiment of the present application;

[0025] Figure 2 is a schematic diagram of writing a tracking log into a video frame according to an embodiment of the present application;

[0026] Figure 3 is a flowchart of another tracking algorithm simulation test method according to an embodiment of the present application;

[0027] Figure 4 Flowchart of a zoom algorithm simulation test method according to an embodiment of the present application;

[0028] Figure 5 is a block diagram of an automatic tracking shooting device according to an embodiment of the present application;

[0029] Figure 6 A schematic diagram of the structure of a terminal device or server suitable for implementing an embodiment of the present application. DETAILED DESCRIPTION

[0030] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] In the field of computer vision analysis, it is generally assumed that the origin of the coordinate system is located in the upper left corner of the screen. In the various embodiments of the present application, unless otherwise stated, the upper left corner is used as the origin of the coordinate system of the screen. Those skilled in the art should know that such a coordinate system setting is not absolutely fixed. When the origin of the coordinate system is set at any position inside or outside the screen, the corresponding technical solutions that can be obtained by simple adjustments to this solution without creative labor are all within the scope of protection of this application.

[0032] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0033] In the embodiment of the present application, the tracking algorithm running in the automatic tracking shooting device 400 is tested. Therefore, this solution uses the chip in the device body that actually participates in the algorithm calculation, that is, the analysis module 402 to perform algorithm simulation, that is, the target detection algorithm and the tracking algorithm are both running in the local chip of the gimbal, so as to obtain calculation results that are completely consistent with the actual working state.

[0034] Figure 1 A flow chart of a tracking algorithm simulation test method according to an embodiment of the present application is shown as follows: Figure 1 As shown:

[0035] S1: Get the collected real-time video.

[0036] Real-time video is the video captured by the camera module 401 in the automatic tracking camera device 400. In a preferred embodiment, the automatic tracking camera device 400 is equipped with two camera devices: the camera module 401 is used to capture and analyze video, preferably using a wide-angle lens to obtain a larger field of view, and is generally not directly provided to the user; the second is the camera module, which is used to capture and display video, which is the captured video that the user can see.

[0037] S2: Analyze the real-time video using a target detection algorithm to obtain a target frame detection log for each frame of the real-time video.

[0038] The target detection algorithm is used to identify and locate the position of human bodies in images. This application does not restrict the specific target detection algorithm used: the YOLOv8 (You Only Look Once version 8) model, as well as recognition algorithms such as CNN (Convolutional Neural Network) and ViT (Vision Transformer), can be used. For each detected human target, the target detection algorithm generates a target bounding box. Typically, the target bounding box is represented by four parameters: (x, y, width, height), where x and y represent the horizontal and vertical coordinates of the target bounding box's center, respectively; width represents the width of the target bounding box; and height represents the height of the target bounding box.

[0039] The target frame detection log is a log of all target frame parameters in each frame of real-time video detected by the target detection algorithm. Each frame has a corresponding target frame detection log. An example of a target frame detection log is as follows:

[0040] [{"time": 1750161291.6964421, "currentYawAngle": 0.0, "currentPitchAngle": 12.0, "yolo": [{"label": 0, "confidence": 0.96, "x":286.0, "y": 869.0, "width": 78.0, "height": 190.0}, {"label": 0, "confidence": 0.96, "x": 1824.0, "y": 836.0, "width": 72.0, "height": 176.0}, {"label": 0, "confidence": 0.95, "x": 1358.0, "y": 736.0, "width": 60.0, "height": 143.0}, {"label": 0, "confidence": 0.94, "x": 1402.0, "y": 670.0, "width": 50.0, "height": 101.0}, {"label": 0, "confidence": 0.87, "x": 668.0, "y": 681.0, "width": 42.0, "height": 108.0}, {"label": 0, "confidence": 0.92,"x": 1210.0, "y": 662.0, "width": 28.0, "height": 92.0}, {"label": 0, "confidence": 0.91, "x": 106.0, "y": 711.0, "width": 26.0, "height": 84.0}]}

[0041] In the above example, the "time" field indicates the timestamp of the video frame corresponding to the log segment; the "currentYawAngle" field indicates the gimbal's pitch angle at the current frame; and the "currentPitchAngle" field indicates the gimbal's yaw angle at the current frame. Following this is detailed information for seven target boxes. The "label" field is the target box label, typically indicating the target type, such as 0 for a human target and 1 for a sphere target. The "confidence" field indicates the confidence level, indicating the model's confidence in the detection result. The "x", "y", "width", and "height" parameters define the target box's position and size.

[0042] S3: Analyze the target frame detection log using a tracking algorithm to obtain a tracking log for each frame of the real-time video, wherein the tracking log includes a tracking point position, and the tracking algorithm includes a first algorithm to be tested.

[0043] The basic logic of the tracking algorithm is to determine whether the shooting angle should be adjusted to track and shoot the key target in real time and continuously by analyzing the movement of the target in consecutive frames. Therefore, the tracking algorithm is based on the analysis of the target frame detection log. For the results of the target frame detection log of each newly input frame, the tracking algorithm may calculate a new tracking strategy. In the embodiment of the present application, the tracking strategy is concretized into a tracking log in a standard format, and each frame has a corresponding tracking log. The tracking log must at least include the tracking point position, that is, based on the analysis results of the current frame, the tracking algorithm believes that the video screen should be adjusted to which position as the center.

[0044] On this basis, the tracking algorithm can calculate the rotation instruction based on the position of the current tracking point in the picture based on the PID (proportion integration differentiation) algorithm, and send the corresponding rotation instruction to the rotation module 403.

[0045] Preferably, the tracking log may also include the displacement speed of one or more target frames specified by a preset strategy of the tracking algorithm.

[0046] The goal of the tracking algorithm is to track the key targets in the scene. Therefore, in a preferred embodiment, it is not necessary to record the speed of each target in the video frame. Instead, according to the preset strategy of the tracking algorithm, the displacement speed of the key target frame that the algorithm focuses on is recorded in the log.

[0047] In one implementation, the tracking algorithm may track several fastest human targets; in another implementation, the tracking algorithm may track a ball target in a video frame; in yet another implementation, the tracking algorithm may refer to both several fastest human targets and the ball target at the same time and select the more suitable one to track.

[0048] Preferably, the complete tracking log may also include an example of the tracking log as follows:

[0049] 2025-06-17 19:54:52.040172

[0050] Frame2 fps16 SZ35 Yaw0.0 Pit12.0

[0051] FFilterP11 mot4 Deque10(V19,x19)(V13,x-12)(V8,x-5)

[0052] BallSz12 BallSuccess

[0053] Frame2 FIN:x1280 y720 V0

[0054] In the above example:

[0055] The first line contains the log date and time stamp;

[0056] The Frame field in the second line indicates the frame number. In the above log, it indicates the second frame of the current video. FPS indicates the processing speed of the current video. In the above log, the current processing speed is 16 frames per second. Yaw and Pit indicate the current pitch and yaw angles.

[0057] The third row of the motion tracking Deque has a total of 10 elements, but because the tracking algorithm in this test only tracks the fastest preset number of human target frames, the Deque only displays the speed of four targets and the x-axis displacement within one frame.

[0058] The fourth row's BallSuccess indicates that the ball target was successfully detected, and BallSz12 indicates that the area of ​​the ball target box is 12.

[0059] In the fifth line, FIN:x1280 y720 is the tracking point position in the current frame determined by the tracking strategy, and v0 is the rotation speed in the rotation instruction calculated based on the tracking point position.

[0060] S4: Writing the target frame detection log and the tracking log into the real-time video to obtain a first video for tracking algorithm simulation testing.

[0061] For each frame of real-time video, the target frame detection log and tracking log corresponding to the current frame are written. Specifically, based on the target frame position and size in the target frame detection log, the target frame rectangle is drawn in the current frame, and the target type is annotated on the target frame. Based on the tracking log, the tracking point position of the current frame is drawn in the image frame in a highlighted form, and the displacement speed of one or more target frames specified by the preset tracking algorithm strategy is annotated on the corresponding target frame. Figure 2 Schematic diagram of writing tracking logs into video frames according to an embodiment of the present application. Figure 2 In the figure, the rectangles represent the target boxes, and the red dots represent the tracking point positions of the current frame. The displacement speeds are marked above the three target boxes.

[0062] In another possible embodiment, the result given by the tracking algorithm is tracking a specified target frame; in this case, the target frame to which the tracking point of the current frame belongs may be highlighted based on the tracking log.

[0063] Writing the tracking point positions from the tracking log into the resulting video allows the tester to clearly see the tracking point status calculated by the tracking algorithm being tested, thereby locating the cause of the abnormal pan / tilt rotation. Preferably, in one possible implementation, the speeds of a predetermined number of the fastest human target frames and / or ball target frames are annotated on the corresponding target frames, allowing the tester to clearly see the specific status of the target frames based on which the tracking algorithm calculated the tracking point positions.

[0064] For example, the tester discovered that in a video shot by a tracking gimbal, the players and the ball on the field were moving to the left, but the gimbal was abnormally rotating to the right. At this time, through the annotations in the tracking log, the tester was able to see that in the current frame, the algorithm did not recognize the competitive ball on the field, but instead recognized the backup ball moving in the opposite direction on the sidelines; the algorithm tracked the ball based on the movement trend of the backup ball, resulting in the tracking point being located at the position of this backup ball, generating a rotation instruction to the right. Based on this simulation test result, the tester was able to obtain the test result that the algorithm was unable to determine the interfering ball, and thus optimized the tracking algorithm in a targeted manner.

[0065] Figure 3 A flowchart of another tracking algorithm simulation test method according to an embodiment of the present application is shown. In a preferred embodiment, the tracking algorithm also includes a second algorithm to be tested. By loading the second algorithm to be tested in step S3 and running it, a second tracking log is obtained and written into the first video, thereby obtaining a second video for tracking algorithm comparison testing.

[0066] Different tracking algorithms may analyze the same video differently. In this embodiment, the tracking logs (i.e., tracking point locations) calculated by different tracking algorithms are written to the real-time video to generate a second video. This allows the tester to visually compare the tracking results of the two tracking algorithms. Preferably, different colors can be used to distinguish the tracking points of different tracking algorithms for a more intuitive display.

[0067] Figure 4 A flowchart of a zoom algorithm simulation test method according to an embodiment of the present application is shown. Figure 4 As shown, using real-time video, tracking point positions, and target box detection logs can be used to repeatedly test zoom algorithms in motion tracking.

[0068] S6: Analyze the target frame detection log and the tracking point position using the zoom algorithm to be tested, write the zoom control result into the real-time video, and obtain a third video for zoom algorithm simulation testing.

[0069] In the field of motion tracking, the zoom algorithm is another important computer vision algorithm besides the tracking algorithm. The zoom algorithm needs to be based on the target detection algorithm. It is an algorithm that determines whether the focal length needs to be adjusted to increase or decrease the field of view based on the position of the tracking point and the area ratio of the target frame in its vicinity. Usually, when independently testing the zoom algorithm, it is necessary to run the target detection algorithm first, and then run the zoom algorithm based on the target detection results. However, when the zoom algorithm is actually running, it is not necessary to run the target detection algorithm separately. Instead, the target detection results used for analysis by the tracking algorithm in the previous stage are directly used. Therefore, in the typical zoom algorithm test, additional computing power is consumed, resulting in the inability to test the true computing power.

[0070] In an embodiment of the present application, the existing target frame detection log is directly imported for zoom algorithm analysis, and the zoom control results are written to the real-time video, so as to simulate the operating efficiency of the algorithm in the actual operating environment as much as possible and obtain test results that are closer to the real environment.

[0071] Figure 5 FIG is a block diagram of an automatic tracking shooting device 400 according to an embodiment of the present application. Figure 5 As shown, the automatic tracking shooting device 400 includes a shooting module 401, an analysis module 402, and a rotation module 403. The automatic tracking shooting device 400 is used to capture real-time video, run algorithms, and generate logs to implement the aforementioned simulation test method.

[0072] Optionally, the automatic tracking shooting device 400 can be an independent device, a component included in an independent device, or a combination of multiple different independent devices. In one possible implementation, the shooting module 401 is implemented as an independent smart terminal or camera, the analysis module 402 is implemented as another independent smart terminal or smart hardware, and the rotation module 403 is implemented as a pan-tilt head or a rotatable bracket. In another possible implementation, the shooting module 401 is implemented as a camera module in a smart terminal, the analysis module 402 is implemented as a computing module in the same smart terminal, and the rotation module 403 is implemented as an intelligent pan-tilt head or a rotatable bracket. In yet another possible implementation, the shooting module 401 is implemented as a camera module in an integrated intelligent camera device, the analysis module 402 is implemented as a computing module in the same integrated intelligent camera device, and the rotation module 403 is implemented as a motion mechanism in the same integrated intelligent camera device.

[0073] The camera module 401 is physically connected to the rotation module 403. This physical connection can be fixed or removable. All connection methods that ensure that the rotation module 403 can directly or indirectly physically influence the movement of the camera module 401 are within the scope of protection of this application. The camera module 401 is configured to continuously capture real-time footage and transmit it to the analysis module 402. Optionally, the camera module 401 may not have the ability to actively transmit information, with the analysis module 402 actively acquiring real-time footage from the camera module 401. The analysis module 402 is configured to load a target detection algorithm and a tracking algorithm to track the game, calculate and determine the position of the tracking point in real time, and transmit rotation instructions to the rotation module 403 based on the determination criteria. The analysis module 402 is also configured to output the target frame detection log of the target detection algorithm and the tracking log of the tracking algorithm. The rotation module 403 is configured to rotate horizontally, i.e., about the vertical axis (z-axis). The rotation module 403 is configured to change the angle of the camera module 401 based on the rotation instructions.

[0074] In another embodiment of the present application, a simulation test system is disclosed, including an automatic tracking and shooting device 400 and a simulation device. The simulation device is communicatively connected to the automatic tracking and shooting device 400, and is suitable for obtaining a target frame detection log and a tracking log and writing them into a real-time video to obtain a first video for simulation testing.

[0075] To test the algorithm in a realistic operating environment, the testing method of this application executes the algorithm in an automatic tracking and shooting device 400, thereby obtaining the true algorithm efficiency. Simultaneously, the algorithm's operating results and the captured real-time video are transmitted to a simulation device, which can be implemented as a computer, such as a terminal device or a server. Within the simulation device, the algorithm log is written to the automatic tracking and shooting device 400, generating a first video for simulation testing.

[0076] In one embodiment, the simulation device is further adapted to analyze the target frame detection log and the tracking point position through the zoom algorithm to be tested, write the zoom control result into the real-time video, and obtain a second video that can be repeatedly simulated and tested.

[0077] Figure 6 A schematic diagram of the structure of a terminal device or server suitable for implementing an embodiment of the present application is shown.

[0078] like Figure 6As shown, the terminal device or server includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 into the random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the terminal device or server are also stored. The CPU 501, ROM 502 and RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0079] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output section 507 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 508 including a hard disk; and a communication section 509 including a network interface card such as a LAN card or a modem. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 510 as needed, so that computer programs read therefrom can be installed into the storage section 508 as needed.

[0080] In particular, according to an embodiment of the present application, the above method flow steps can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present application are executed.

[0081] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: 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 of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0082] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the aforementioned module, program segment or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0083] The units or modules described in the embodiments of the present application may be implemented in software or hardware. The units or modules described may also be provided in a processor. The names of these units or modules do not, in certain circumstances, limit the units or modules themselves.

[0084] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device. The computer-readable storage medium stores one or more programs, which, when used by one or more processors, execute the methods described in the present application.

[0085] As another aspect, an embodiment of the present application further provides a computer program product, which implements any of the above-mentioned embodiment methods when the computer program / instructions are executed by a processor.

[0086] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application involved in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned application concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied for in this application.

Claims

1. A test method for visual algorithm simulation, characterized in that: include: S1: Get the collected real-time video; S2: Analyze the real-time video using an object detection algorithm to obtain an object frame detection log for each frame of the real-time video; S3: Analyze the target frame detection log using a tracking algorithm to obtain a tracking log for each frame of the real-time video, wherein the tracking log includes a tracking point position, and the tracking algorithm includes a first algorithm to be tested; S4: Based on the tracking log, the tracking point position of the current frame is drawn in the image frame of the real-time video in a highlighted form, and the target frame detection log and the tracking log are written into the real-time video to obtain a first video for tracking algorithm simulation testing.

2. The method according to claim 1, wherein The target frame detection log includes the target type, position and size of each target frame.

3. The method according to claim 1, wherein The tracking log also includes the displacement speed of one or more target frames specified by the preset strategy of the tracking algorithm; the one or more target frames specified by the preset strategy of the tracking algorithm include: a preset number of human target frames and / or ball target frames with the fastest speed.

4. The method according to claim 1, wherein The tracking algorithm further includes a second algorithm to be tested, and the method further includes: S5: Writing the second tracking log obtained by executing the S3 step based on the second algorithm to be tested into the first video to obtain a second video for tracking algorithm comparison test.

5. The method according to any one of claims 1 to 4, wherein The method further comprises: S6: Analyze the target frame detection log and the tracking point position using the zoom algorithm to be tested, write the zoom control result into the real-time video, and obtain a third video for zoom algorithm simulation testing.

6. An automatic tracking shooting device, characterized in that: The system comprises a shooting module, an analysis module and a rotation module. The shooting module is physically connected to the rotation module, the rotation module is suitable for horizontal rotation, the shooting module is suitable for continuously capturing real-time video and sending the real-time video frame by frame to the analysis module; the analysis module is suitable for loading a target detection algorithm and a tracking algorithm to track and shoot the game, generate a rotation instruction, and send the rotation instruction to the rotation module; the analysis module is also suitable for analyzing the real-time video through the target detection algorithm to obtain a target frame detection log for each frame of the real-time video, and analyzing the target frame detection log through the tracking algorithm to obtain a tracking log for each frame of the real-time video, the tracking log including the tracking point position, the tracking log being used to draw the tracking point position of the current frame in a highlighted form in the image frame, and the tracking algorithm including a first algorithm to be tested; the rotation module is suitable for changing the angle of the shooting module according to the rotation instruction.

7. A simulation test system, characterized in that: include: The automatic tracking shooting device according to claim 6; A simulation device is communicatively connected to the automatic tracking and shooting device, and is suitable for obtaining the target frame detection log and the tracking log and writing them into real-time video to obtain a first video for simulation testing.

8. The system according to claim 7, wherein: The simulation device is also suitable for analyzing the target frame detection log and tracking point position through the zoom algorithm to be tested, writing the zoom control result into the real-time video, and obtaining a third video for zoom algorithm simulation testing.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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