Trajectory positioning method, computer device and storage medium

Abstract

The application relates to the technical field of trajectory positioning, and discloses a trajectory positioning method, computer equipment and a storage medium. The method comprises the following steps: acquiring a first image sequence and a second image sequence; determining each image group based on the first image sequence and the second image sequence, wherein the image group comprises a first image in the first image sequence and a second image in the second image sequence, and the first image is matched with the second image; determining a first intersection measurement result and a second intersection measurement result according to a target object in the first image and a target object in the second image; determining a coplanar difference based on the first intersection measurement result and the second intersection measurement result; and determining a trajectory positioning result based on the coplanar difference, the first intersection measurement result and the second intersection measurement result. The trajectory positioning result can be obtained through the first image sequence and the second image sequence, and accurate measurement of the movement trajectory of the target object can be realized.

Description

Technical Field

[0001] This invention relates to the field of trajectory positioning technology, and in particular to a trajectory positioning method, computer equipment, and storage medium. Background Technology

[0002] A drone swarm is a low-altitude, slow-speed, small cluster of aircraft, also known as a "low, slow, small" multi-target formation. Its flight altitude is generally below 1,000 meters, and it is subject to many interference factors. Its radar cross-section is small, making it difficult to detect and capture.

[0003] Drone swarm trajectory measurement is a crucial module in the entire optoelectronic detection system, enhancing the detection and analysis capabilities of anti-drone systems. However, drone swarms often contain multiple targets, making it difficult to accurately pinpoint their movement trajectories. Summary of the Invention

[0004] Based on this, it is necessary to address the problem of the difficulty in accurately locating the motion trajectory of multiple target objects in existing technologies, and propose a trajectory positioning method, computer equipment, and storage medium.

[0005] Firstly, a trajectory localization method is provided, the method comprising:

[0006] Obtain the first image sequence and the second image sequence;

[0007] Based on the first image sequence and the second image sequence, each image group is determined, wherein the image group includes a first image in the first image sequence and a second image in the second image sequence, and the first image matches the second image;

[0008] Based on the target objects in the first image and the target objects in the second image, determine the first intersection measurement result and the second intersection measurement result;

[0009] Based on the first intersection measurement results and the second intersection measurement results, the coplanarity difference is determined;

[0010] Based on the coplanar difference, the first intersection measurement result, and the second intersection measurement result, the trajectory positioning result is determined.

[0011] In a second aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the trajectory positioning method described above.

[0012] Thirdly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the trajectory positioning method described above.

[0013] The trajectory localization method proposed in this invention acquires a first image sequence and a second image sequence, then determines various image groups based on the first and second image sequences. Each image group includes a first image from the first image sequence and a second image from the second image sequence. The first and second images are matched. Then, based on the target object in the first and second images, a first intersection measurement result and a second intersection measurement result are determined. Based on the first and second intersection measurement results, a coplanar difference is determined. Finally, based on the coplanar difference, the first and second intersection measurement results, the trajectory localization result is determined. This method can obtain the trajectory localization result from the first and second image sequences, achieving accurate measurement of the target object's motion trajectory. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] in:

[0016] Figure 1 This is a diagram illustrating the application environment of the trajectory localization method in one embodiment;

[0017] Figure 2 Here is a flowchart of a trajectory localization method in one embodiment;

[0018] Figure 3 This is a structural block diagram of a computer device in one embodiment;

[0019] Figure 4 This is a structural block diagram of a computer device in another embodiment. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The trajectory positioning method provided in this embodiment of the invention can be applied to, for example... Figure 1 In this application environment, client 110 communicates with server 120 via a network. Server 120 receives a first image sequence and a second image sequence from client 110. Then, based on the first and second image sequences, server 120 determines image groups, where each image group includes a first image from the first image sequence and a second image from the second image sequence. The first and second images are matched. Next, server 120 determines a first intersection measurement result and a second intersection measurement result based on the target object in the first and second images. Based on the first and second intersection measurement results, server 120 determines the coplanar difference. Finally, based on the coplanar difference, the first and second intersection measurement results, server 120 determines the trajectory positioning result. This allows for accurate measurement of the target object's motion trajectory by obtaining the trajectory positioning result from the first and second image sequences. Client 110 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. Server 120 can be implemented using a standalone server or a server cluster consisting of multiple servers. The present invention will now be described in detail through specific embodiments.

[0022] Please see Figure 2 As shown, Figure 2 A schematic flowchart of a trajectory positioning method provided in an embodiment of the present invention includes the following steps:

[0023] Step S101: Obtain the first image sequence and the second image sequence;

[0024] The first image sequence was acquired by a measurement substation, and the second image sequence was acquired by another measurement substation. As an example, the measurement substation may be a high frame rate visible light camera (frame rate ≥ 1000fps).

[0025] Step S102: Based on the first image sequence and the second image sequence, determine each image group, wherein the image group includes a first image in the first image sequence and a second image in the second image sequence, and the first image matches the second image;

[0026] As an example, if the first image and the second image are acquired at the same time, then the first image and the second image are considered a match. As another example, if the frame number of the first image in the first image sequence is the same as the frame number of the second image in the second image sequence, then the first image and the second image are considered a match. It should be noted that when acquiring each image in the first image sequence, each image can be sorted, and the same applies to the second image sequence.

[0027] Step S103: Determine the first intersection measurement result and the second intersection measurement result based on the target object in the first image and the target object in the second image;

[0028] In this embodiment, the target object can be a swarm of drones, and the number of target objects can be greater than or equal to 0. By measuring the intersection of the target objects in the first image and the target objects in the second image, a first intersection measurement result and a second intersection measurement result can be obtained.

[0029] In one embodiment, the step of determining the first intersection measurement result and the second intersection measurement result based on the target object in the first image and the target object in the second image includes:

[0030] Step S1031: Determine the first position based on the target object in the first image, and determine the second position based on the target object in the second image;

[0031] The first position can be the center position of the target object in the first image, which can be represented by coordinate data. The second position can be the center position of the target object in the second image, which can be represented by coordinate data.

[0032] As an example, the first image detects M target objects, and the set of center positions of the M target objects is represented as follows: The second image detects N target objects, and the set of center positions of the N target objects is as follows:

[0033] Step S1032: Perform intersection measurement based on the first position and the second position to obtain the first intersection measurement result;

[0034] Step S1033: Perform an intersection measurement between the second position and the first position to obtain a second intersection measurement result.

[0035] As an example, the azimuth angles measured during the first image sequence and the second image sequence are A and B, respectively. 1 and A 2 The pitch angles are E 1 and E 2Given that the dimension of the conversion between image pixels and the azimuth and elevation angles of the measurement station is U, then the azimuth angle of the i-th target in the first image of the first image sequence is... pitch angle is The azimuth angle of the j-th target in the second image of the second image sequence is pitch angle is

[0036] Given that the geodetic coordinates of the first image sequence and the second image sequence are (X... #1 ,Y #1 Z #1 ) and (X #2 ,Y #2 Z #2 If the first image and the second image are given, then the intersection measurement result of the i-th target in the first image and the j-th target in the second image is: Should This is the result of the first intersection measurement. The formula for calculating the result of the first intersection measurement is as follows:

[0037]

[0038] Similarly, the intersection measurement results of the j-th target in the second image of the second image sequence and the i-th target in the first image of the first image sequence are calculated. Should This is the result of the second intersection measurement. The formula for calculating the result of the second intersection measurement is as follows:

[0039]

[0040] Step S104: Based on the first intersection measurement result and the second intersection measurement result, determine the coplanarity difference;

[0041] In this embodiment, the coplanar difference is calculated using the first intersection measurement results and the second intersection measurement results, thereby obtaining the coplanar difference.

[0042] As an example, the coplanar difference d ij The calculation formula is as follows:

[0043]

[0044] Step S105: Determine the trajectory positioning result based on the coplanar difference, the first intersection measurement result, and the second intersection measurement result.

[0045] In this embodiment, the trajectory positioning results corresponding to the target object in the first image and the target object in the second image can be obtained by using the coplanar difference, the first intersection measurement result, and the second intersection measurement result.

[0046] The trajectory localization method proposed in this embodiment acquires a first image sequence and a second image sequence, then determines each image group based on the first image sequence and the second image sequence. The image group includes a first image in the first image sequence and a second image in the second image sequence. The first image and the second image are matched. Then, based on the target object in the first image and the target object in the second image, a first intersection measurement result and a second intersection measurement result are determined. Based on the first intersection measurement result and the second intersection measurement result, the coplanar difference is determined. Finally, based on the coplanar difference, the first intersection measurement result, and the second intersection measurement result, the trajectory localization result is determined. This method can obtain the trajectory localization result through the first image sequence and the second image sequence, and achieve accurate measurement of the motion trajectory of the target object.

[0047] In one embodiment, the step of determining the trajectory positioning result based on the coplanar difference, the first intersection measurement result, and the second intersection measurement result includes:

[0048] Step 1051: If the coplanar difference is less than the preset measurement accuracy, then construct the structure according to the first intersection measurement result and the second intersection measurement result to obtain the location point structure, and store the location point structure in the preset storage unit.

[0049] Among them, the measurement accuracy can be the measurement accuracy of the measurement substation, which can be preset by technicians, and the location point structure is a type of structure.

[0050] In this embodiment, it is first determined whether the coplanar difference is less than the preset measurement accuracy. If the coplanar difference is less than the preset measurement accuracy, the structure is constructed based on the first intersection measurement result and the second intersection measurement result to obtain the location point structure, and the location point structure is stored in the preset storage unit.

[0051] Step 1052: When the storage unit meets the preset conditions, determine the trajectory positioning result based on the various location point structures within the storage unit.

[0052] In one implementation, when the number of location point structures whose past clustering state was the second initial value reaches a preset number in the storage unit, the storage unit satisfies the preset condition. This means that the number of location point structures whose past clustering state was the second initial value has been satisfied, and clustering of these location point structures is required. In another implementation, location point structures other than those whose past clustering state was the second initial value can be deleted from the storage unit, or only a preset number can be retained, which can be one.

[0053] In one implementation, the step of constructing a structure based on the first intersection measurement result and the second intersection measurement result to obtain a location point structure includes:

[0054] Step 10511: Calculate the location point data based on the first intersection measurement result and the second intersection measurement result, and determine the frame number of the first image corresponding to the first intersection measurement result in the first image sequence.

[0055] Step 10512: The frame sequence number, the location point data, the preset current clustering state, and the preset past clustering state are used as a location point structure, wherein the preset current clustering state is set to a first initial value, and the preset past clustering state is set to a second initial value.

[0056] In this embodiment, the first intersection measurement result and the second intersection measurement result are added together to obtain the summed result, and the summed result is divided by 2 to obtain the location point data. The frame number refers to the frame number of the first image corresponding to the first intersection measurement result in the first image sequence, and it can also be the frame number of the second image corresponding to the second intersection measurement result in the second image sequence. Both the first initial value and the second initial value are preset.

[0057] As an example, the location point structure is represented as p(pos,fn,ID,preID).

[0058]

[0059] Where fcount is the frame number of the current image, UNCLASSIFIED represents the first and second initial values, p.preID represents the past clustering state, p.ID represents the current clustering state, p.fn represents the frame number, and p.pos represents the location data.

[0060] In one embodiment, the step of determining the trajectory positioning result based on each of the location point structures within the storage unit includes:

[0061] Step 10521: When the storage unit meets the preset conditions, the location point structure whose past clustering state is the second initial value is used as the first location point structure, and the location point structure whose past clustering state is not the second initial value is used as the second location point structure.

[0062] In this embodiment, the location point structures whose past clustering state was the second initial value mean that no clustering process has been performed. The location point structures whose past clustering state was not the second initial value mean that clustering process has been performed.

[0063] Step 10522: Based on the size relationship of the frame sequence number, select at least one second position point structure from each second position point structure as the third position point structure;

[0064] For example, the second location point structure with the largest frame sequence number is selected from all the second location point structures and used as the third location point structure. It should be noted that this means that when performing clustering processing on the first and third location point structures, there are location point structures that participated in the previous clustering.

[0065] Step 10523: The DBSCAN trajectory clustering algorithm is used to cluster the first location point structure and the third location point structure to obtain the clustering results.

[0066] Specifically, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) trajectory clustering algorithm can be used for clustering.

[0067] Step 10524: Determine the trajectory positioning result based on the clustering result, the first location point structure, and the third location point structure.

[0068] In one embodiment, the step of determining the trajectory positioning result based on the clustering result, the first location point structure, and the third location point structure includes:

[0069] Step A: Based on the clustering results, update the current clustering state of the first location point structure and the current clustering state of the third location point structure, and use the updated first location point structure and the third location point structure as the first structure.

[0070] As an example, all aggregation results are represented as {p k .ID},{p k The range of the .ID value is (1,2,L,C), where C is the maximum cluster value (C is the number of classification types).

[0071] Step B: If a second structure and a third structure exist, then update the first structure according to the second structure and the third structure, and use the updated first structure as the target location point structure. Here, the second structure refers to the first structure whose past clustering state was not the second initial value, and the third structure refers to the first structure with the same current clustering state as the second structure.

[0072] The second structure refers to the first structure whose previous clustering state was not the second initial value, which means that the second structure participated in the previous clustering.

[0073] For example, the current clustering state of the third structure can be set to the past clustering state of the second structure. Then, the third and second structures can be used as the first structure again, thereby updating the first structure. Updating the first structure means that the trajectory localization results can be continuous each time.

[0074] Step C: Determine the trajectory positioning result based on the target location point structure.

[0075] In one embodiment, the step of determining the trajectory positioning result based on the target location point structure includes: step C1, dividing the target location point structure into various point sets based on the current clustering state of the target location point structure;

[0076] For example, put the target location point structures that are currently in the same clustering state into the same point set.

[0077] Step C2: In the point set, if there are target location point structures with the same frame number, the target location point structures with the same frame number are taken as the first target structure, and the target location point structures other than the first target structure are determined as the second target structure.

[0078] For example, in the point set, if there are target location point structures with the same frame number, then the target location point structure with the same frame number is designated as the first target structure. In the point set, the target location point structures other than the first target structure are identified as the second target structure.

[0079] Step C3: Determine the trajectory positioning result based on the first target structure and the second target structure.

[0080] In one embodiment, the step of determining the trajectory positioning result based on the first target structure and the second target structure includes:

[0081] Step C31: Calculate the distance between the first target structure and the second target structure, and determine the first target structure corresponding to the smallest distance;

[0082] For each first target structure, calculate the distance between each second target structure and the first target structure. Among the obtained distances, determine the distance with the smallest value, and then determine the first target structure corresponding to the smallest distance.

[0083] Step C32: In the point set, delete the first target structure except the one corresponding to the smallest distance, and set the past clustering state of each location point structure in the point set to be the same as the current clustering state to obtain the target point set;

[0084] In the point set, all first target structures except those corresponding to the first target structure with the smallest distance are deleted. The past clustering state of each location point structure in the point set is then set to the same as the current clustering state, resulting in the target point set. It should be noted that all first target structures except those corresponding to the first target structure with the smallest distance are false targets and therefore need to be deleted.

[0085] Step C33: Use the location point data of each location point structure in the target point set as the trajectory positioning result.

[0086] As an example, the location point data of each location point structure in the target point set is stored in a preset trajectory recording unit, that is, the trajectory positioning result is stored in the preset trajectory recording unit.

[0087] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When executed by the processor, the computer program implements the functions or steps of a trajectory localization method on the server side.

[0088] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When executed by the processor, the computer program implements the functions or steps of a trajectory positioning method on the client side.

[0089] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs the following steps:

[0090] Obtain the first image sequence and the second image sequence;

[0091] Based on the first image sequence and the second image sequence, each image group is determined, wherein the image group includes a first image in the first image sequence and a second image in the second image sequence, and the first image matches the second image;

[0092] Based on the target objects in the first image and the target objects in the second image, determine the first intersection measurement result and the second intersection measurement result;

[0093] Based on the first intersection measurement results and the second intersection measurement results, the coplanarity difference is determined;

[0094] Based on the coplanar difference, the first intersection measurement result, and the second intersection measurement result, the trajectory positioning result is determined.

[0095] The trajectory localization method proposed in this embodiment acquires a first image sequence and a second image sequence, then determines each image group based on the first image sequence and the second image sequence. The image group includes a first image in the first image sequence and a second image in the second image sequence. The first image and the second image are matched. Then, based on the target object in the first image and the target object in the second image, a first intersection measurement result and a second intersection measurement result are determined. Based on the first intersection measurement result and the second intersection measurement result, the coplanar difference is determined. Finally, based on the coplanar difference, the first intersection measurement result, and the second intersection measurement result, the trajectory localization result is determined. This method can obtain the trajectory localization result through the first image sequence and the second image sequence, and achieve accurate measurement of the motion trajectory of the target object.

[0096] In one embodiment, a computer-readable storage medium is provided that stores a computer program, which, when executed by a processor, performs the following steps:

[0097] Obtain the first image sequence and the second image sequence;

[0098] Based on the first image sequence and the second image sequence, each image group is determined, wherein the image group includes a first image in the first image sequence and a second image in the second image sequence, and the first image matches the second image;

[0099] Based on the target objects in the first image and the target objects in the second image, determine the first intersection measurement result and the second intersection measurement result;

[0100] Based on the first intersection measurement result and the second intersection measurement result, the coplanar difference is determined; based on the coplanar difference, the first intersection measurement result, and the second intersection measurement result, the trajectory positioning result is determined.

[0101] The trajectory localization method proposed in this embodiment acquires a first image sequence and a second image sequence, then determines each image group based on the first image sequence and the second image sequence. The image group includes a first image in the first image sequence and a second image in the second image sequence. The first image and the second image are matched. Then, based on the target object in the first image and the target object in the second image, a first intersection measurement result and a second intersection measurement result are determined. Based on the first intersection measurement result and the second intersection measurement result, the coplanar difference is determined. Finally, based on the coplanar difference, the first intersection measurement result, and the second intersection measurement result, the trajectory localization result is determined. This method can obtain the trajectory localization result through the first image sequence and the second image sequence, and achieve accurate measurement of the motion trajectory of the target object.

[0102] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0103] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0104] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A trajectory positioning method, characterized by, The trajectory localization method includes: Obtain the first image sequence and the second image sequence; Based on the first image sequence and the second image sequence, each image group is determined, wherein the image group includes a first image in the first image sequence and a second image in the second image sequence, and the first image matches the second image; Based on the target objects in the first image and the target objects in the second image, determine the first intersection measurement result and the second intersection measurement result; Based on the first intersection measurement results and the second intersection measurement results, the coplanarity difference is determined; Based on the coplanar difference, the first intersection measurement result, and the second intersection measurement result, the trajectory positioning result is determined; The step of determining the trajectory positioning result based on the coplanar difference, the first intersection measurement result, and the second intersection measurement result includes: If the coplanar difference is less than the preset measurement accuracy, then a structure is constructed based on the first intersection measurement result and the second intersection measurement result to obtain a location point structure, and the location point structure is stored in a preset storage unit; When the storage unit meets the preset conditions, the trajectory positioning result is determined according to each of the location point structures in the storage unit; The step of constructing a structure based on the first intersection measurement result and the second intersection measurement result to obtain a location point structure includes: Based on the first intersection measurement result and the second intersection measurement result, the location point data is obtained, and the frame number of the first image corresponding to the first intersection measurement result in the first image sequence is determined. The frame number, the location point data, the preset current clustering state, and the preset past clustering state are used as a location point structure, wherein the preset current clustering state is set to a first initial value, and the preset past clustering state is set to a second initial value. The step of determining the trajectory positioning result based on each of the location point structures in the storage unit includes: The location point structure whose past clustering state was the second initial value is used as the first location point structure, and the location point structure whose past clustering state was not the second initial value is used as the second location point structure. Based on the size relationship of the frame sequence number, at least one second position point structure is selected from each second position point structure as the third position point structure; The DBSCAN trajectory clustering algorithm is used to cluster the first location point structure and the third location point structure to obtain the clustering results. Based on the clustering results, the first location point structure, and the third location point structure, the trajectory positioning result is determined; The step of determining the trajectory positioning result based on the clustering result, the first location point structure, and the third location point structure includes: Based on the clustering results, update the current clustering state of the first location point structure and the current clustering state of the third location point structure, and use the updated first location point structure and the third location point structure as the first structure; If a second structure and a third structure exist, the first structure is updated based on the second structure and the third structure, and the updated first structure is used as the target location point structure. Here, the second structure refers to the first structure whose past clustering state was not the second initial value, and the third structure refers to the first structure whose current clustering state is the same as that of the second structure. The current clustering state of the third structure is set as the past clustering state of the second structure, and the third structure and the second structure are used again as the first structure, thereby updating the first structure. Based on the target location point structure, the trajectory positioning result is determined.

2. The trajectory positioning method according to claim 1, characterized by, The step of determining the first intersection measurement result and the second intersection measurement result based on the target object in the first image and the target object in the second image includes: Based on the target object in the first image, determine the first position; based on the target object in the second image, determine the second position. The first intersection measurement result is obtained by performing an intersection measurement between the first position and the second position; The second intersection measurement result is obtained by performing an intersection measurement between the second position and the first position.

3. The trajectory positioning method according to claim 1, wherein, The step of determining the trajectory positioning result based on the target location point structure includes: Based on the current clustering state of the target location point structure, the target location point structure is divided to obtain various point sets; In the set of points, if there are target location point structures with the same frame number, the target location point structures with the same frame number are taken as the first target structure, and the target location point structures other than the first target structure are determined as the second target structure. Based on the first target structure and the second target structure, the trajectory positioning result is determined.

4. The trajectory positioning method according to claim 3, characterized in that, The step of determining the trajectory positioning result based on the first target structure and the second target structure includes: Calculate the distance between the first target structure and the second target structure, and determine the first target structure corresponding to the smallest distance; In the point set, delete the first target structure except the one corresponding to the smallest distance, and set the past clustering state of each location point structure in the point set to be the same as the current clustering state to obtain the target point set; The location data of each location point structure in the target point set is used as the trajectory positioning result.

5. 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 computer program, it implements the steps of the trajectory positioning method as described in any one of claims 1 to 4.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the trajectory positioning method as described in any one of claims 1 to 4.

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