Vehicle-machine collaborative target loss tracking method, device, equipment and storage medium thereof

Abstract

The present application discloses a vehicle-machine collaborative target loss tracking method, device, equipment and storage medium thereof, the method comprising: by identifying the target to be tracked, sending the identified target information to the control system on the drone, and simultaneously starting the PID target tracking control module on the drone to track the target; when the drone loses the tracking target, restoring the QR code tracking state by tracking and predicting the trajectory of the unmanned vehicle; when the target is still lost after tracking the predicted trajectory of the unmanned vehicle, the flight altitude of the drone is increased and hovered, and the unmanned vehicle is searched through the target tracking algorithm to restore the QR code tracking state. The above scheme provided by the present application can effectively solve the problem of the drone losing the target caused by the unmanned vehicle QR code being temporarily blocked or the QR code being out of the camera shooting space range, and greatly improves the vehicle-machine collaborative system by combining QR code tracking, trajectory prediction tracking, and target tracking methods.

Description

Technical Field

[0001] The present invention relates to the field of vehicle-machine collaborative technology, and in particular to a vehicle-machine collaborative target lost tracking method, device, equipment and storage medium thereof. Background Art

[0002] In recent years, the field of vehicle-machine collaboration has become a research hotspot. Unmanned vehicles and drones have important applications in civil, anti-terrorism and explosion prevention, and military fields. Drones are often required to track unmanned vehicles to perform corresponding tasks. In the process of tracking the target, the motion state of the unmanned vehicle or drone may change due to strong winds, uneven roads, etc., causing the drone to lose the tracking target during the tracking process, which will greatly reduce work efficiency. In the research of tracking algorithms, how to enable the drone to recapture the tracking target after losing the tracking target is also a problem that must be considered and solved.

[0003] The existing patent CN106023257A proposes a target tracking method based on a rotary-wing UAV platform. This method effectively realizes the rapid and accurate online real-time tracking of moving targets by the rotary-wing UAV platform through multi-scale sample collection and combined with a real-time updated classifier; during the tracking process, the maximum classifier response value of the current frame and its change compared with the maximum classifier response value of the previous frame, combined with the tracking performance judgment result of the previous frame, are used to determine whether the tracking performance of the current frame is stable. When the tracking performance is unstable, the classification result of the classifier is corrected in time, which can effectively prevent the loss of the tracking target due to occlusion during the target tracking process.

[0004] At the same time, an existing target tracking algorithm based on L1 graph semi-supervised collaborative training uses the tracking results to collect positive samples representing the target and negative samples representing the background in the first few frames, and extracts the color and texture features of the samples to construct two fully redundant views. When a new frame arrives, the candidate area is randomly sampled as an unlabeled sample using the particle filter as a model. The classifier is then trained on the two views using the semi-supervised learning algorithm based on the L1 graph to calculate the similarity of the unlabeled samples. Then, several unlabeled samples with the lowest similarity are selected as new labeled negative samples to update the classifier. Finally, the classifiers under different views independently calculate the similarity of the unlabeled samples, and the final result is obtained by fusion using their similarity entropy as the weight. An existing remote control simulation control method based on the SBUS protocol is designed for unmanned ground vehicles. A tracking scheme based on proportional label recognition is studied for real-time performance and occlusion problems. The scheme tracks the labeled target without occlusion. When occlusion occurs, the scheme tracks the color features around the label. The accuracy of the tracking algorithm and the occlusion problem are greatly improved. Finally, the scheme is applied to heterogeneous systems. An existing four-rotor control method based on SBUS protocol is proposed to maintain the original stability of the UAV. A UGV with mechanical wheels and its control method are proposed. In view of the problems of real-time performance and occlusion, a tracking scheme based on QR code (quick response code) recognition is proposed. Finally, the scheme is applied to heterogeneous systems. An existing remote control simulation control method based on SBUS protocol is proposed. A detailed control method is designed for unmanned ground vehicles. In view of the real-time performance and occlusion problems, a tracking scheme based on proportional label recognition is studied. The scheme tracks the label target without occlusion. When occlusion occurs, the scheme tracks the color features around the label. The accuracy of the tracking algorithm and the occlusion problem are improved. Finally, the scheme is applied to heterogeneous systems.

[0005] However, many existing technologies are based on optimizing tracking algorithms in order to reduce the probability of losing the tracking target, and basically no method is proposed to solve the problem of target loss. At the same time, the existing technology is aimed at the algorithm research of drone tracking, and does not consider the vehicle-machine collaboration as a whole system, and the use of a single target recognition algorithm cannot cope with various scenarios. For example, when the flying altitude of the drone is different, the accuracy of the target recognition algorithm is also different, which will result in the inability to accurately track the target at different altitudes. Summary of the invention

[0006] In view of the above-mentioned defects or deficiencies in the prior art, it is desired to provide a vehicle-machine collaborative target lost tracking method, device, equipment and storage medium thereof.

[0007] In the first aspect, an embodiment of the present application provides a vehicle-machine collaborative target lost tracking method, the method comprising: identifying the target to be tracked through a target recognition module on the drone, sending the identified target information to the control system on the drone, and simultaneously starting the PID target tracking control module on the drone to perform target tracking; when the drone loses the tracking target, restoring the QR code tracking state by tracking and predicting the trajectory of the unmanned vehicle; when the predicted trajectory of the unmanned vehicle is tracked and is still in the target lost state, increasing the flight altitude of the drone and circling, searching for the unmanned vehicle through the target tracking algorithm to restore the QR code tracking state.

[0008] In one of the embodiments, the target recognition module is composed of a two-dimensional code detection algorithm, including any one of artag, apritag, or aruco two-dimensional code detection algorithms.

[0009] In one of the embodiments, identifying the target to be tracked by the target recognition module on the drone includes: identifying the QR code image on the target unmanned vehicle to be tracked by the fisheye monocular camera on the drone, and outputting the position and posture of the QR code in the camera coordinate system.

[0010] In one of the embodiments, when the drone loses the tracking target, the QR code tracking state is restored by tracking and predicting the unmanned vehicle trajectory, including: the model prediction unmanned vehicle trajectory module on the unmanned vehicle predicts the trajectory point coordinates of the unmanned vehicle within a preset time, and sends the trajectory point coordinates to the predicted trajectory tracking control module on the drone; the predicted trajectory tracking control module converts the trajectory point coordinates into the trajectory in the drone body coordinate system, and then tracks the predicted trajectory points one by one through the PID control method until the drone finds the QR code.

[0011] In one of the embodiments, when the unmanned vehicle's trajectory is still in a target lost state after tracking and predicting, the drone's flight altitude is increased and hovered, and the unmanned vehicle is searched for through a target tracking algorithm to restore the QR code tracking state, including: determining whether there is an obstruction directly above the unmanned vehicle through a laser radar on the unmanned vehicle, and if there is an obstruction, controlling the unmanned vehicle to move a preset distance in the target direction to an unobstructed area and stop there; increasing the drone's flight altitude and hovering in the air with a radius of R, recognizing the image through the telephoto camera on the drone, and directly tracking the unmanned vehicle using the target tracking algorithm KCF, CSK, or C-COT algorithm; when the unmanned vehicle is recognized, the drone stops hovering and moves the unmanned vehicle to the center of the image through PID control, and then the drone lowers its altitude to a preset altitude, and when the QR code on the unmanned vehicle is recognized, it lowers its altitude again to a preset altitude, resumes the normal tracking program, and starts the unmanned vehicle to start the mission.

[0012] On the second aspect, an embodiment of the present application also provides a vehicle-machine collaborative target lost tracking device, which includes: a target recognition module, used to identify the target to be tracked, send the recognized target information to the control system on the drone, and simultaneously start the PID target tracking control module on the drone to perform target tracking; a tracking module, used to restore the QR code tracking state by tracking and predicting the trajectory of the unmanned vehicle after the drone loses the tracking target; a target search module, used to increase the flight altitude of the drone and hover when it is still in the target lost state after tracking the predicted trajectory of the unmanned vehicle, and restore the QR code tracking state by searching for the unmanned vehicle through the target tracking algorithm.

[0013] In a third aspect, an embodiment of the present application further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements any method described in the embodiments of the present application.

[0014] In a fourth aspect, an embodiment of the present application further provides a computer device and a computer-readable storage medium on which a computer program is stored, wherein the computer program is used to implement any method described in the embodiments of the present application when the computer program is executed by a processor.

[0015] Beneficial effects of the present invention:

[0016] The vehicle-machine collaborative target loss tracking method provided by the present invention can effectively solve the problem of the unmanned aerial vehicle losing the target caused by the unmanned vehicle's QR code being temporarily blocked or the QR code being out of the camera's shooting space range. By combining QR code tracking, trajectory prediction tracking, and target tracking methods, the vehicle-machine collaborative system and the robustness of the unmanned vehicle tracking the unmanned aerial vehicle and the person are greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0018] Figure 1 A schematic diagram of the process of the vehicle-machine collaborative target loss tracking method provided in an embodiment of the present application is shown;

[0019] Figure 2 An exemplary structural block diagram of a vehicle-machine cooperative target loss tracking device 200 according to an embodiment of the present application is shown;

[0020] Figure 3 A schematic diagram showing the structure of a computer system suitable for implementing a terminal device of an embodiment of the present application is shown;

[0021] Figure 4Another schematic diagram of the process of the vehicle-machine cooperative target loss tracking method provided in an embodiment of the present application is shown;

[0022] Figure 5 A schematic diagram of a vehicle kinematic model provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0024] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0025] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0026] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0028] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "upper", "lower", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only implementation method.

[0029] Please refer to Figure 1 Combined with Figure 4 , Figure 1 A flow chart of a vehicle-machine collaborative target loss tracking method provided in an embodiment of the present application is shown.

[0030] like Figure 1 As shown, the method includes:

[0031] Step 110, identifying the target to be tracked through the target identification module on the drone, sending the identified target information to the control system on the drone, and starting the PID (proportional, integral, derivative) target tracking control module on the drone to track the target;

[0032] Step 120, when the UAV loses the tracking target, the QR code tracking state is restored by tracking and predicting the trajectory of the unmanned vehicle;

[0033] Step 130, when the unmanned vehicle is still in the target lost state after tracking the predicted trajectory, the flight altitude of the unmanned vehicle is increased and hovered, and the unmanned vehicle is searched through the target tracking algorithm to restore the QR code tracking state.

[0034] The PID target tracking control module in this application has a tracking height of h 0 The current position, current heading angle, expected position and expected heading angle of the UAV in the body coordinate system are output through PID control to determine the speed and heading angular velocity of the UAV in the x-axis, y-axis and z-axis directions in the body coordinate system.

[0035] Δw t =w expect -w current

[0036] I t =I t-1 +Δw t

[0037] I t =cliff(I t ,-c,+c),

[0038] D t =Δw t -D t-1

[0039]

[0040] w current is the current position or posture, w expect is the desired position or posture, I t is the integral quantity, D t is the difference quantity, For speed, cliff(I t ,-c,+c) is the truncation function, I t Truncated to constant value plus or minus c.

[0041] The above technical solution can effectively solve the problem of the drone losing the target caused by the unmanned vehicle's QR code being temporarily blocked or the QR code being out of the camera's shooting range. By combining QR code tracking, trajectory prediction tracking, and target tracking methods, the vehicle-machine collaborative system is greatly improved, and the robustness of the unmanned vehicle tracking the drone and the person is greatly improved.

[0042] In some embodiments, the target recognition module in the present application is composed of a two-dimensional code detection algorithm, including any one of artag, apritag, or aruco two-dimensional code detection algorithms.

[0043] In some embodiments, identifying the target to be tracked by a target recognition module on the drone includes:

[0044] The fisheye monocular camera on the drone recognizes the QR code image on the target unmanned vehicle to be tracked, and outputs the position and posture of the QR code in the camera coordinate system.

[0045] In some embodiments, when the drone loses the tracking target, restoring the QR code tracking state by tracking and predicting the unmanned vehicle trajectory includes:

[0046] The model prediction module on the unmanned vehicle predicts the coordinates of the unmanned vehicle's trajectory points within a preset time, and sends the coordinates of the trajectory points to the prediction trajectory tracking control module on the drone;

[0047] The predicted trajectory tracking control module converts the trajectory point coordinates into the trajectory in the drone body coordinate system, and then tracks the predicted trajectory points one by one through the PID control method until the drone finds the QR code.

[0048] Further, refer to Figure 5 As shown, taking the vehicle kinematic model as an example

[0049]

[0050] Autonomous vehicle status The control amount of the unmanned vehicle u=[v r δ f ], the unmanned vehicle kinematic model Use forward Euler discretization to get ξ K+1 =f(ξ K ,u k ), the control amount of the unmanned vehicle is u=[v r ω],

[0051]

[0052] By inputting the control quantity of N steps into the discrete unmanned vehicle mathematical model, the state quantity of the unmanned vehicle at each step can be obtained.

[0053] The predicted trajectory tracking control module will climb to a height h after the target is lost. 1 (h 1 >h 0 ) Expand the camera's field of view, convert the trajectory predicted by the trajectory prediction module into the trajectory of the drone's body coordinate system, and then track the predicted trajectory points one by one through the PID control method. When the QR code is re-recognized, the module is stopped and the height h is restored. 0 , and start the normal QR code tracking procedure.

[0054] In some embodiments, when the unmanned vehicle's trajectory is predicted after tracking and is still in a target lost state, the drone's flight altitude is increased and hovered, and the unmanned vehicle is searched for through a target tracking algorithm to restore the QR code tracking state, including: determining whether there is an obstruction directly above the unmanned vehicle through the laser radar on the unmanned vehicle, and if there is an obstruction, controlling the unmanned vehicle to move a preset distance in the target direction to an unobstructed area and stop there; increasing the drone's flight altitude and hovering in the air with a radius of R, recognizing the image through the telephoto camera on the drone, and directly tracking the unmanned vehicle using the target tracking algorithm KCF, CSK, or C-COT algorithm; when the unmanned vehicle is identified, the drone stops hovering and moves the unmanned vehicle to the center of the image through PID control, and then the drone lowers its altitude to a preset altitude, and when the QR code on the unmanned vehicle is identified, it lowers its altitude again to a preset altitude, resumes the normal tracking program, and starts the unmanned vehicle to start the mission.

[0055] Specifically, the target search module is started when the target is still lost after the aforementioned prediction trajectory tracking control module has tracked N prediction trajectory points. At this time, the vehicle-mounted vertical upward laser radar is first used to determine whether there is an obstruction directly above the unmanned vehicle. If there is an obstruction, the unmanned vehicle is controlled to move a short distance in the target direction to an unobstructed area and stop in place. At the same time, the drone climbs vertically to a height h2 (h2>h1) again. At this time, the ordinary monocular camera has lost focus and hovers in the air with a radius of R. The telephoto camera is used to recognize the image, and the common target tracking algorithms KCF, CSK, or C-COT are used to directly track the unmanned vehicle. When the unmanned vehicle is recognized, the drone stops hovering and moves the unmanned vehicle to the center of the image through PID control. Then the drone lowers its height to h1, and after recognizing the QR code, it lowers its height again to h0, resumes the normal tracking program, and starts the unmanned vehicle to perform the task.

[0056] Further, refer to Figure 2 , Figure 2 An exemplary structural block diagram of a vehicle-machine collaborative target loss tracking device 200 according to an embodiment of the present application is shown.

[0057] like Figure 2 As shown, the device comprises:

[0058] The target identification module 210 is used to identify the target to be tracked, send the identified target information to the control system on the UAV, and start the PID target tracking control module on the UAV to track the target;

[0059] Tracking module 220, used to restore the QR code tracking state by tracking and predicting the unmanned vehicle trajectory when the unmanned vehicle loses the tracking target;

[0060] The target search module 230 is used to increase the flying altitude of the UAV and hover when the UAV is still in the target lost state after tracking the predicted trajectory of the UAV, and to search for the UAV through the target tracking algorithm to restore the QR code tracking state.

[0061] It should be understood that the units or modules described in the device 200 are similar to those described in the reference Figure 1 The various steps in the described method correspond to each other. Therefore, the operations and features described above for the method are also applicable to the device 200 and the units contained therein, and will not be repeated here. The device 200 can be pre-implemented in a browser or other security application of an electronic device, or loaded into a browser or its security application of an electronic device by downloading or the like. The corresponding units in the device 200 can cooperate with the units in the electronic device to implement the solution of the embodiment of the present application.

[0062] Reference below Figure 3 , which shows a schematic diagram of the structure of a computer system 300 of a terminal device or server suitable for implementing an embodiment of the present application.

[0063] like Figure 3 As shown, the computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage part 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the system 300 are also stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0064] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.

[0065] In particular, according to the embodiments of the present disclosure, the above reference Figure 1-2The described process may be implemented as a computer software program. For example, an embodiment of the present disclosure includes a vehicle-machine collaborative target loss tracking method, which includes a computer program tangibly embodied on a machine-readable medium, the computer program including a method for executing Figure 1-2 In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 309 and / or installed from the removable medium 311.

[0066] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of the code, and the aforementioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order 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 flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0067] The units or modules involved in the embodiments described in the present application may be implemented by software or by hardware. The described units or modules may also be provided in a processor, for example, may be described as: a processor includes a first sub-region generation unit, a second sub-region generation unit, and a display area generation unit. Among them, the names of these units or modules do not constitute a limitation on the unit or module itself in certain circumstances, for example, the display area generation unit may also be described as "a unit for generating a display area for text based on a first sub-region and a second sub-region".

[0068] As another aspect, the present application further provides a computer-readable storage medium, which may be the computer-readable storage medium included in the aforementioned device in the above embodiment; or may be a computer-readable storage medium that exists independently and is not assembled into the device. The computer-readable storage medium stores one or more programs, and the aforementioned programs are used by one or more processors to execute the text generation method for transparent window envelopes described in the present application.

[0069] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A vehicle-machine cooperative target loss tracking method, characterized in that: The method includes: Control the drone to track at a height h0, identify the target to be tracked through the target recognition module on the drone, send the identified target information to the control system on the drone, and start the PID target tracking control module on the drone to track the target; wherein the target recognition module is composed of a QR code detection algorithm; When the drone loses the tracking target, it controls the drone to climb to a height of h1, predicts the coordinates of the unmanned vehicle's trajectory points within the preset time, and converts the coordinates of the trajectory points into the trajectory of the drone body coordinate system, and then tracks the predicted trajectory points one by one until the drone finds the QR code and lowers the altitude to h0 again, thereby restoring the QR code tracking state by tracking and predicting the unmanned vehicle's trajectory, h1>h0; When the unmanned vehicle trajectory is still in the target lost state after tracking and predicting, if there is an obstruction directly above the unmanned vehicle, the unmanned vehicle is controlled to move a preset distance in the target direction to an unobstructed area and stop there. When there is no obstruction directly above the unmanned vehicle, the flight altitude of the drone is increased to h2 and hovered, h2>h1, and the unmanned vehicle is searched through the target tracking algorithm. When the unmanned vehicle is identified, the drone is lowered to h1, and after the QR code is identified, the altitude is lowered again to h0 to restore the QR code tracking state.

2. The vehicle-machine cooperative target loss tracking method according to claim 1 is characterized in that: The two-dimensional code detection algorithm includes any one of artag, apritag, or aruco two-dimensional code detection algorithms.

3. The vehicle-machine cooperative target loss tracking method according to claim 1, characterized in that: The target identification module on the drone identifies the target to be tracked, including: The fisheye monocular camera on the drone recognizes the QR code image on the target unmanned vehicle to be tracked, and outputs the position and posture of the QR code in the camera coordinate system.

4. The vehicle-machine cooperative target loss tracking method according to claim 1, characterized in that: The target tracking algorithm adopts KCF, CSK, or C-COT algorithm.

5. A vehicle-machine cooperative target lost tracking device, characterized in that: The device includes: The target recognition module, which is composed of a QR code detection algorithm, is used to identify the target to be tracked at the UAV tracking height h0, send the identified target information to the control system on the UAV, and start the PID target tracking control module on the UAV to track the target; The tracking module is used to control the drone to climb to a height of h1 when the drone loses the tracking target, predict the coordinates of the unmanned vehicle's trajectory points within a preset time, and convert the coordinates of the trajectory points into the trajectory of the drone body coordinate system, and then track the predicted trajectory points one by one until the drone finds the QR code and lowers the altitude to h0 again, thereby realizing the restoration of the QR code tracking state by tracking and predicting the unmanned vehicle's trajectory, h1>h0; The target search module is used to control the unmanned vehicle to move a preset distance in the target direction to an unobstructed area and stop there if there is an obstruction directly above the unmanned vehicle when the unmanned vehicle's trajectory is still in the target lost state after tracking. When there is no obstruction directly above the unmanned vehicle, the flight altitude of the drone is increased to h2 and hovered, h2>h1. The unmanned vehicle is searched through the target tracking algorithm. When the unmanned vehicle is identified, the drone is lowered to h1. After the QR code is identified, the altitude is lowered again to h0 to restore the QR code tracking state.

6. The vehicle-machine cooperative target lost tracking device according to claim 5, characterized in that: The target tracking algorithm adopts KCF, CSK, or C-COT algorithm.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, the computer program being used to: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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