Image positioning method, system and network device

Abstract

The application provides an image positioning method, system and network device, and belongs to the field of video monitoring. The image positioning method comprises the following steps: acquiring a monitoring image collected by a camera in a monitoring area; acquiring a target object from the monitoring image, acquiring a position of the target object in the monitoring area, and obtaining a first positioning result; acquiring a relative speed of the target object relative to an auxiliary measuring point in the monitoring area, determining a second positioning result of the target object relative to the auxiliary measuring point according to the relative speed; and determining the position of the target object in the monitoring area according to the first positioning result and the second positioning result. The above method is based on multi-camera video image data, converts the monitoring area into a three-dimensional grid matrix, and calculates the position of the target in the monitoring area at each time and space through auxiliary measurement of the auxiliary measuring point, so as to form a more accurate spatial moving track of the target.

Description

Technical Field

[0001] This invention relates to the field of video surveillance technology, and in particular to an image positioning method, system, and network device. Background Technology

[0002] Currently, microwave radar is generally used to locate targets that are not equipped with positioning terminals. However, microwave radar is expensive and inconvenient to maintain. In addition, existing video surveillance mainly relies on personnel on duty to observe the monitoring screen, or on the monitoring platform to determine which camera area a person or object is in, which cannot achieve high-precision positioning, let alone form a movement trajectory.

[0003] The aforementioned existing technologies have low positioning accuracy, low efficiency, and poor real-time performance when locating targets that do not carry positioning terminals. Summary of the Invention

[0004] This invention proposes an image positioning method, system, and network device to solve the problems of low accuracy and low efficiency in the prior art when locating targets without positioning terminals.

[0005] To solve the above-mentioned technical problems, the present invention is implemented as follows:

[0006] In a first aspect, embodiments of the present invention provide an image localization method, the method comprising:

[0007] Acquire surveillance images captured by cameras within the monitored area;

[0008] The target object is obtained from the monitoring image, and the position of the target object in the monitoring area is obtained to obtain a first positioning result;

[0009] The relative velocity of the target object with respect to auxiliary measurement points within the monitoring area is obtained, and a second positioning result of the target object with respect to the auxiliary measurement points is determined based on the relative velocity.

[0010] Based on the first positioning result and the second positioning result, the location of the target object in the monitoring area is determined.

[0011] Secondly, embodiments of the present invention provide an image positioning system, the system comprising:

[0012] The monitoring image acquisition module is used to acquire monitoring images captured by cameras within the monitored area;

[0013] The first positioning module is used to obtain a target object from the monitoring image, obtain the position of the target object in the monitoring area, and obtain a first positioning result;

[0014] The second positioning module is used to obtain the relative speed of the target object with respect to the auxiliary measurement points within the monitoring area, and to determine the second positioning result of the target object with respect to the auxiliary measurement points based on the relative speed.

[0015] The third positioning module is used to determine the location of the target object in the monitoring area based on the first positioning result and the second positioning result.

[0016] Thirdly, embodiments of the present invention provide a network device, including a transceiver and a processor;

[0017] The transceiver is used to receive monitoring images captured by cameras within the monitoring area;

[0018] The processor is configured to obtain a target object from the monitoring image, obtain the position of the target object in the monitoring area, and obtain a first positioning result;

[0019] The processor is further configured to acquire the relative velocity of the target object with respect to the auxiliary measurement points within the monitoring area, and determine a second positioning result of the target object relative to the auxiliary measurement points based on the relative velocity;

[0020] The processor is further configured to determine the location of the target object in the monitoring area based on the first positioning result and the second positioning result.

[0021] Fourthly, embodiments of the present invention provide a network device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the image localization method of the first aspect described above.

[0022] Fifthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the image localization method of the first aspect described above.

[0023] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0024] The image positioning method provided by this invention acquires monitoring images captured by cameras within a monitoring area; obtains a target object from the monitoring images; obtains the position of the target object within the monitoring area to obtain a first positioning result; obtains the relative velocity of the target object relative to auxiliary measurement points within the monitoring area; determines a second positioning result of the target object relative to the auxiliary measurement points based on the relative velocity; and determines the position of the target object within the monitoring area based on the first positioning result and the second positioning result. This method, based on video image data from multiple cameras, transforms the monitoring area into a three-dimensional grid matrix and uses auxiliary measurement points for assisted measurement to calculate the target's position in the monitoring area at various times and spaces, forming a more accurate spatial movement trajectory of the target. Attached Figure Description

[0025] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0026] Figure 1 A flowchart of an image localization method provided in an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of camera deployment for an image positioning method provided in an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram illustrating an application scenario of an image localization method provided in an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of the structure of an image positioning system provided in an embodiment of the present invention;

[0030] Figure 5 This is a schematic diagram illustrating another application scenario of the image localization method provided in this embodiment of the invention;

[0031] Figure 6 This is a schematic diagram of the operation of an image positioning system provided in an embodiment of the present invention;

[0032] Figure 7 This is a schematic diagram of the structure of a network device provided in an embodiment of the present invention. Detailed Implementation

[0033] 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.

[0034] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0035] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0036] The image localization method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0037] Please refer to Figure 1 , Figure 1 A flowchart of an image localization method provided by an embodiment of the present invention is shown. The method includes:

[0038] Step 11: Obtain the surveillance images captured by the cameras in the monitored area.

[0039] Specifically, before acquiring surveillance images, cameras need to be deployed in the monitored area. This involves selecting suitable locations within the area and installing one or more cameras, either professional or non-professional, and connecting them to power and network. After deployment, camera information, including camera ID and installation location (such as latitude and longitude), can be obtained.

[0040] To improve monitoring accuracy, two or more cameras can be grouped together to monitor the area from different directions. Figure 2 A schematic diagram of a camera deployment is shown. Figure 2 Two cameras, cameras 21 and 22, were deployed in the middle.

[0041] When using two or more cameras, the distance between them should be maximized during installation to form an angle, allowing for monitoring of object movement from different directions. This effectively improves positioning accuracy and the scope of the monitoring area. High resolution, sensitivity, and appropriately increasing the number of cameras are all deployment strategies to ensure consistent trajectory. Step 12: Obtain the target object from the monitoring image, and determine the position of the target object within the monitoring area to obtain the first positioning result.

[0042] Specifically, before target recognition, based on the images captured by the camera, the monitoring area is analyzed to form a spatial matrix with the horizontal plane as the basic plane and gridded at the centimeter level. After the target object appears, the position of the target object in the monitoring area can be obtained, and the first positioning result is obtained.

[0043] Step 13: Obtain the relative velocity of the target object with respect to the auxiliary measurement points within the monitoring area, and determine the second positioning result of the target object relative to the auxiliary measurement points based on the relative velocity.

[0044] Due to inherent errors in the camera device itself, or positional deviations after prolonged use, errors may occur in the first positioning result. By introducing auxiliary measurement points, the position of the target object relative to the auxiliary measurement points is determined based on its relative velocity, thereby obtaining the second positioning result. There are at least two auxiliary measurement points.

[0045] Step 14: Determine the location of the target object in the monitoring area based on the first positioning result and the second positioning result.

[0046] The second positioning result obtained through auxiliary measurement points is compared with the first positioning result. This includes matching and correcting the measured positions of each auxiliary measurement point with the auxiliary measurement point data captured by the camera; comparing the calculated relative position or latitude / longitude position with the relative position or latitude / longitude position of the target object measured by the auxiliary measurement points to determine the position of the target object within the monitored area. Furthermore, if there are discrepancies, the auxiliary point measurements can be used as the standard.

[0047] The image positioning method provided in this invention can transform the monitored area into a three-dimensional mesh matrix based on video image data from multiple cameras. Through image analysis and object contour algorithms, it calculates the target's spatial and temporal position within the monitored area, forming the target's spatial movement trajectory. Furthermore, auxiliary measurement points can be used to correct the position of the object captured by the cameras, thereby obtaining a more accurate target motion trajectory.

[0048] Optionally, in some embodiments, the method further includes acquiring surveillance images captured by cameras within the monitored area for the first time:

[0049] Initialize the spatial data of the monitored area;

[0050] Spatial images are extracted from the monitoring images, and grid calculations are performed on the plane where the monitoring area is located to obtain the gridded monitoring area plane.

[0051] Specifically, after initially acquiring the surveillance images captured by the cameras within the monitored area, data initialization is performed, spatial location data is analyzed, and a gridded monitoring area plane is constructed based on the monitored area to achieve equal spatial distribution.

[0052] For example, a monitoring platform or terminal equipped with the image positioning method provided in this embodiment can extract spatial images from the video acquired by the camera, perform grid calculations on the plane of the monitoring range, such as calculating the relative positions of multiple quadrilateral points by using the included angle and the distance from points on the horizontal ground to the camera, marking the horizontal distance positions of these feature points relative to the camera, and equally subdividing each side of the quadrilateral to form a grid on the plane. It is worth noting that if the camera has included angle and distance measurement functions, the camera can calculate them independently; if the camera does not have these functions, the network is calculated and formed by using the camera's position parameters and measurement data from auxiliary measurement points.

[0053] Optionally, in some embodiments, the method further includes determining multiple auxiliary measurement points within the monitoring area and obtaining auxiliary measurement point data within the monitoring area, including: selecting the farthest straight line within the gridded monitoring area plane and deploying three or more auxiliary measurement points;

[0054] Within the monitored area, select the nearest straight line and deploy three or more auxiliary measurement points;

[0055] Before obtaining the relative speed of the target object relative to the auxiliary measurement points within the monitoring area, the method further includes: obtaining auxiliary measurement point data within the monitoring area, wherein the auxiliary measurement point data includes at least one of the following: latitude and longitude, altitude, and relative position of each auxiliary measurement point to each camera.

[0056] For example, refer to Figure 3 Within the monitored area, select the furthest straight line and deploy three auxiliary measurement points (A, B, C), and select the closest straight line and deploy three auxiliary measurement points (D, E, F). After deployment, auxiliary point data can be obtained, including latitude and longitude data, altitude, or relative position data with respect to the monitoring camera.

[0057] For example, based on images captured by cameras, the platform analyzes the monitored area to create a spatial matrix with the horizontal plane as the basic plane and gridded at the centimeter level. The deployment of auxiliary measurement points is to improve reference accuracy. When a target, such as a person or object, appears in the matrix position, its position information can be obtained. By analyzing the positional relationship between the target and two of the auxiliary points, a high-precision displacement position can be obtained. The positions between the target and each auxiliary measurement point, as well as the camera, can be measured separately for reference in relative displacement positioning of the target.

[0058] It is worth noting that professional cameras are equipped with satellite positioning modules, levels, and network time reception functions. The level can automatically and roughly calculate the distance between two points on the horizontal plane. However, as the usage time changes, the position of the camera may shift, which will cause the automatically calculated target position distance to deviate. Using auxiliary measurement points to assist in the calculation can obtain more accurate position information.

[0059] Optionally, in some embodiments, obtaining the relative velocity of the target object relative to the auxiliary measurement point and determining the relative position of the target object relative to the auxiliary measurement point based on the relative velocity includes:

[0060] Based on the relative velocity between the target object and the target auxiliary measurement point at the target time, and based on the relative acceleration between the target object and the target auxiliary measurement point at the target time, the relative position of the target object relative to the target auxiliary measurement point at the target time is obtained;

[0061] After obtaining the relative position of the target object with respect to at least two auxiliary measurement points, the coordinate position of the target object in the plane of the monitoring area is obtained based on the relative position as the second positioning result.

[0062] Specifically, using the formula ∫X(t)=V(t)*b(t)+1 / 2b(t)*t 2 Calculate the relative position of the target object with respect to the target auxiliary measurement point at time t;

[0063] Where X represents the target object, V(t) is the relative velocity between the target object and the target auxiliary measurement point at time t, and ∫X(t) is the relative position of the target object with respect to the target auxiliary measurement point at time t;

[0064] b(t) is the acceleration of the target object relative to the target auxiliary measurement point at time t;

[0065]

[0066] ΔXf(x) represents the minimum speed, where -x represents the maximum speed;

[0067]

[0068] X(x) represents the average acceleration of the target object, n represents the number of times the target object's velocity is sampled, and a n The target object's location is represented by L, and the distance between the target object and the auxiliary measurement point is represented by V. n This represents the average velocity of the target object relative to the auxiliary measurement point. Optionally, in some embodiments, the method further includes:

[0069] Based on the spatiotemporal information of the target object within the monitoring image, the location data of the target object at various times is determined, and the trajectory of the target object in the monitoring area is formed according to the time development nodes.

[0070] Specifically, the spatiotemporal information of the target object includes the target object's position in the monitoring area at each measurement moment, as well as the target object's spatial displacement information. All of the above information is recorded and stored in the image positioning platform.

[0071] Optionally, in some embodiments, the method further includes;

[0072] The target object is obtained from the monitoring image, and face recognition and / or object contour recognition are performed on the target object to obtain the recognition result;

[0073] The identification result is associated with the information in the database. If the association fails, the information of the target object is created based on the identification result, and the database is updated.

[0074] Based on the information association, the spatiotemporal information of the target object is stored.

[0075] Specifically, depending on the type of the target object, different preset recognition algorithms can be used for identification. The recognition results are then associated with the database. For example, if the target object is a person, it is associated with specific person information; if the target object is an object, it is associated with object information. If there is no corresponding association in the target object database, the target object information is created. For example, if the target object A, Zhang San, a warehouse employee, is identified, his trajectory in the monitored area from 15:00 to 15:10 on April 18, 2022 is recorded.

[0076] Optionally, in some embodiments, the method further includes: archiving and storing the spatiotemporal trajectory of the target object in the monitoring area according to the target object information; or archiving and storing the spatiotemporal trajectory of each target object according to the time of occurrence in the monitoring area; so as to facilitate trajectory query of the target object, or query trajectory information in the target monitoring time.

[0077] In summary, the image positioning method provided by this invention can achieve joint monitoring by deploying multiple cameras, transforming the monitored area into a three-dimensional grid matrix. Through image analysis and object contour algorithms, it calculates the position of the target in the monitored area at various times and spaces, obtaining the target's movement trajectory. Furthermore, it forms the trajectory of the target object in the monitored area according to time development nodes. Additionally, it can correct the position of the object captured by the cameras through auxiliary measurement points, thereby obtaining a more accurate target movement trajectory. It can also identify the target object, such as the specific attributes of the target person or the type of the target object, generating a spatiotemporal trajectory containing the target object's information.

[0078] refer to Figure 4 The invention also provides an image positioning system 40, comprising:

[0079] The monitoring image acquisition module 41 is used to acquire monitoring images captured by cameras within the monitoring area;

[0080] The first positioning module 42 is used to obtain a target object from the monitoring image, obtain the position of the target object in the monitoring area, and obtain a first positioning result;

[0081] The second positioning module 43 is used to obtain the relative speed of the target object with respect to the auxiliary measurement point in the monitoring area, and to determine the second positioning result of the target object with respect to the auxiliary measurement point based on the relative speed.

[0082] The third positioning module 44 is used to determine the location of the target object in the monitoring area based on the first positioning result and the second positioning result.

[0083] Optionally, in some embodiments, the image positioning system 40 further includes:

[0084] The meshing module 45 is used after initially acquiring surveillance images captured by cameras within the monitored area:

[0085] Initialize the spatial data of the monitored area;

[0086] Spatial images are extracted from the monitoring images, and grid calculations are performed on the plane where the monitoring area is located to obtain the gridded monitoring area plane.

[0087] Optionally, in some embodiments, the image positioning system 40 further includes:

[0088] The auxiliary measurement module 46 is used to select the farthest straight line and deploy three or more auxiliary measurement points within the gridded monitoring area plane;

[0089] Within the monitored area, select the nearest straight line and deploy three or more auxiliary measurement points;

[0090] Before obtaining the relative speed of the target object relative to the auxiliary measurement points within the monitoring area, the method further includes: obtaining auxiliary measurement point data within the monitoring area, wherein the auxiliary measurement point data includes at least one of the following: latitude and longitude, altitude, and relative position of each auxiliary measurement point to each camera.

[0091] Optionally, in some embodiments, the image positioning system 40 further includes:

[0092] Target recognition module 47 is used for:

[0093] The target object is obtained from the monitoring image, and face recognition and / or object contour recognition are performed on the target object to obtain the recognition result;

[0094] The identification result is associated with the information in the database. If the association fails, the information of the target object is created based on the identification result, and the database is updated.

[0095] Based on the information association, the spatiotemporal information of the target object is stored.

[0096] Optionally, in some embodiments, the image positioning system 40 further includes:

[0097] The trajectory generation module 48 is used to determine the location data of the target object at various times based on the spatiotemporal information of the target object in the monitoring image, and to form the trajectory of the target object in the monitoring area according to the time development nodes.

[0098] Optionally, in some embodiments, reference is made to... Figure 6 This is a schematic diagram of the operation flow of the image positioning system 40. When a target object enters the monitoring area, the image positioning system performs the following operations:

[0099] Step 61: Upload image data of the target objects entering the monitoring area;

[0100] It can be uploaded to the system's database or to cloud data;

[0101] Step 62: Identify the target object using face recognition or object contour recognition algorithms;

[0102] Step 63: If the target object information can be confirmed, associate the target object information in the database;

[0103] Step 64: If the target object information cannot be confirmed, create new target object information and update it to the database;

[0104] Step 65: Analyze and store the spatiotemporal location of the target object using image algorithms and auxiliary measurement points;

[0105] Step 66: Draw the movement trajectory of the target object according to the timeline.

[0106] The system first obtains the identity information through facial recognition in videos or images. If the person is not found in the system, they are numbered and recorded, and linked to previous and subsequent data to facilitate the generation of trajectory reports later. Combined with video or image positioning technology, it can achieve a clear and accurate positioning result; without terminal support, the system can intelligently analyze images, calculate the relative position of the target object to the reference point, and obtain the high-precision position of the person or object.

[0107] In addition, the system processes images to obtain the location and trajectory data of people or objects; and various applications can be added to realize functions such as early warning, supervision, and behavior analysis according to business needs.

[0108] Through the various modules of the image positioning system 40 described above, the various processes of the image positioning method embodiment provided in the present invention can be implemented, and the same technical effect can be achieved. To avoid repetition, they will not be described again here.

[0109] Please refer to Figure 7 The present invention also provides a network device 70, including a processor 71, a memory 72, and a computer program stored in the memory 72 and executable on the processor 71. When the computer program is executed by the processor 71, it implements the various processes of the above-described image positioning method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0110] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described image positioning method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0111] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0112] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0113] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. An image localization method, characterized in that, The method includes: Acquire surveillance images captured by cameras within the monitored area; The target object is obtained from the monitoring image, and the position of the target object in the monitoring area is obtained to obtain a first positioning result; The relative velocity of the target object with respect to auxiliary measurement points within the monitoring area is obtained, and a second positioning result of the target object relative to the auxiliary measurement points is determined based on the relative velocity. Obtaining the relative velocity of the target object with respect to the auxiliary measurement points within the monitoring area includes: performing grid calculations on the plane containing the monitoring area to obtain a gridded monitoring area plane; selecting the farthest straight line within the gridded monitoring area plane and deploying three or more auxiliary measurement points; and selecting the nearest straight line within the monitoring area plane and deploying three or more auxiliary measurement points. Based on the first positioning result and the second positioning result, the location of the target object in the monitoring area is determined.

2. The image localization method according to claim 1, characterized in that, This also includes after the initial acquisition of surveillance images captured by cameras within the monitored area: Initialize the spatial data of the monitored area; Spatial images are extracted from the monitoring images, and grid calculations are performed on the plane where the monitoring area is located to obtain the gridded monitoring area plane.

3. The image localization method according to claim 2, characterized in that, Before obtaining the relative speed of the target object relative to the auxiliary measurement points within the monitoring area, the method further includes: obtaining auxiliary measurement point data within the monitoring area, wherein the auxiliary measurement point data includes at least one of the following: latitude and longitude, altitude, and relative position of each auxiliary measurement point to each camera.

4. The image localization method according to claim 1, characterized in that, The method further includes; The target object is obtained from the monitoring image, and face recognition and / or object contour recognition are performed on the target object to obtain the recognition result; The identification result is associated with the information in the database. If the association fails, the information of the target object is created based on the identification result, and the database is updated. Based on the information association, the spatiotemporal information of the target object is stored.

5. The image localization method according to claim 1, characterized in that, The step of obtaining the relative velocity of the target object relative to the auxiliary measurement point and determining the relative position of the target object relative to the auxiliary measurement point based on the relative velocity includes: Based on the relative velocity between the target object and the target auxiliary measurement point at the target time, and based on the relative acceleration between the target object and the target auxiliary measurement point at the target time, the relative position of the target object relative to the target auxiliary measurement point at the target time is obtained; After obtaining the relative position of the target object with respect to at least two target auxiliary measurement points, the coordinate position of the target object in the plane of the monitoring area is obtained based on the relative position as the second positioning result.

6. The image localization method according to claim 4, characterized in that, The method further includes: Based on the spatiotemporal information of the target object within the monitoring image, the location data of the target object at various times is determined, and the trajectory of the target object in the monitoring area is formed according to the time development nodes.

7. An image positioning system, characterized in that, The system includes: The monitoring image acquisition module is used to acquire monitoring images captured by cameras within the monitored area; The first positioning module is used to obtain a target object from the monitoring image, obtain the position of the target object in the monitoring area, and obtain a first positioning result; The second positioning module is used to obtain the relative velocity of the target object with respect to auxiliary measurement points within the monitoring area, and to determine a second positioning result of the target object relative to the auxiliary measurement points based on the relative velocity. Obtaining the target object relative to the auxiliary measurement points within the monitoring area includes: performing grid calculations on the plane containing the monitoring area to obtain a gridded monitoring area plane; selecting the farthest straight line within the gridded monitoring area plane and deploying three or more auxiliary measurement points; and selecting the nearest straight line within the monitoring area plane and deploying three or more auxiliary measurement points. The third positioning module is used to determine the location of the target object in the monitoring area based on the first positioning result and the second positioning result.

8. A network device, characterized in that, Includes transceivers and processors; The transceiver is used to receive monitoring images captured by cameras within the monitoring area; The processor is configured to obtain a target object from the monitoring image, obtain the position of the target object in the monitoring area, and obtain a first positioning result; The processor is further configured to acquire the relative velocity of the target object with respect to auxiliary measurement points within the monitoring area, and determine a second positioning result of the target object relative to the auxiliary measurement points based on the relative velocity. Acquiring the target object with respect to the auxiliary measurement points within the monitoring area includes: performing grid calculations on the plane containing the monitoring area to obtain a gridded monitoring area plane; selecting the farthest straight line within the gridded monitoring area plane and deploying three or more auxiliary measurement points; and selecting the nearest straight line within the monitoring area plane and deploying three or more auxiliary measurement points. The processor is further configured to determine the location of the target object in the monitoring area based on the first positioning result and the second positioning result.

9. A network device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the image localization method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the image localization method as described in any one of claims 1 to 6.

Images