Monitoring point optimization method and device fusing GIS technology and face recognition, and terminal

Abstract

The invention relates to a monitoring point optimization method and device fusing a GIS technology and face recognition, and a terminal. According to the method, the GIS technology and the face recognition technology are adopted to realize grid management of a monitoring area; data is provided for abnormal track line identification through equipment parameter information and monitoring data of monitoring equipment; according to the monitoring data and the snapshot data, a track speed and an abnormal track line are analyzed, a monitoring blind area in a monitoring area is identified, virtual point location construction is carried out on the abnormal track line, a construction recommendation numerical value is obtained, a monitoring range map after accurate monitoring point location construction optimization is obtained, the coverage rate and monitoring efficiency of a monitoring network are remarkably improved, and the monitoring efficiency of the monitoring network is improved. The monitoring blind area is effectively reduced, and the construction and operation cost of the monitoring system is reduced; the technical problems that traditional monitoring point location layout depends on artificial experience and on-site investigation, the efficiency is low, and it is difficult to ensure that a monitoring network can comprehensively cover a key area, and consequently resources are wasted are solved.

Description

Technical Field

[0001] The present application relates to the field of intelligent monitoring technology, and in particular to a monitoring point optimization method, device and terminal integrating GIS technology and face recognition. Background Art

[0002] With the rapid development of urbanization, the importance of public security monitoring has become increasingly prominent. However, the traditional layout of monitoring points mainly relies on manual experience and on-site inspections. This method is not only inefficient, but also difficult to ensure that the monitoring network can fully cover key areas and effectively capture important facial information. This leads to the frequent appearance of monitoring blind spots and unnecessary waste of monitoring resources.

[0003] In order to solve these problems and improve the efficiency and coverage of the monitoring system, it is urgent to adopt scientific methods based on data analysis to optimize the layout of monitoring points. The advancement of geographic information systems (GIS) and computer vision technology has made it possible to achieve this goal, making the construction of monitoring points more accurate and efficient. Summary of the invention

[0004] The present application provides a monitoring point optimization method, device and terminal integrating GIS technology and face recognition, which is used to solve the technical problems that the traditional monitoring point layout relies on manual experience and on-site investigation, is inefficient, and it is difficult to ensure that the monitoring network can fully cover key areas, resulting in waste of resources.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] On the one hand, a monitoring point optimization method integrating GIS technology and face recognition is provided, comprising the following steps:

[0007] Obtain map data of the monitoring area and equipment parameter information of each monitoring device in the monitoring area, and use GIS technology to perform regional gridding and equipment location marking processing on the monitoring area in turn according to all the equipment parameter information to obtain the monitoring range area of ​​each monitoring device;

[0008] Acquire monitoring data of each monitoring device in each monitoring range area, filter data of the same person from all the monitoring data and sort the data by time to obtain snapshot data of the same person; the monitoring data includes a snapshot image, a snapshot time, and a position of the person corresponding to the snapshot image and a means of transportation used by the person;

[0009] Obtaining the distance data and time difference between any two adjacent captured images and the normal speed of the vehicle used by the acquiring personnel from the captured data, calculating the trajectory speed according to the distance data and the time difference, and judging whether the trajectory line where the two adjacent captured images are located is an abnormal trajectory line according to the trajectory speed and the normal speed;

[0010] If the trajectory line where the two adjacent captured images are located is an abnormal trajectory line, it is judged whether the abnormal trajectory line in the monitoring range area is within the monitoring range of the monitoring device corresponding to the captured image; if not, virtual points are constructed on the abnormal trajectory line in the monitoring range area and recommended construction values ​​are obtained to obtain a monitoring range map with optimized monitoring points.

[0011] Preferably, constructing virtual points on the abnormal trajectory line in the monitoring range area and obtaining recommended construction values, and obtaining a monitoring range map with optimized monitoring points includes:

[0012] Constructing a plurality of virtual points on each abnormal trajectory line in the monitoring range area, acquiring corresponding monitoring equipment in the grid of the monitoring range area where each virtual point is located, and obtaining a device data set;

[0013] Obtaining an angle between each monitoring device in the device data set and the corresponding virtual point, and judging whether the virtual point is within the field of view of the corresponding monitoring device according to a comparison between the angle and the field of view of the device parameter information in the corresponding monitoring device;

[0014] If the virtual point is within the field of view of the corresponding monitoring device, the construction recommendation value corresponding to the virtual point is increased by 1 until the virtual point traverses all the monitoring devices in the device data set to obtain the construction recommendation value corresponding to the virtual point;

[0015] The GIS technology is used to overlay and display all abnormal trajectory lines and all corresponding virtual points and construction recommended values ​​on the monitoring range area to obtain a monitoring range map with optimized monitoring points.

[0016] Preferably, the monitoring point optimization method integrating GIS technology and face recognition includes acquiring monitoring data of each of the monitoring devices using a computer vision algorithm in the monitoring range area.

[0017] Preferably, calculating the trajectory speed according to the distance data and the time difference comprises: calculating the trajectory speed by dividing the distance data by the time difference.

[0018] Preferably, judging whether the abnormal trajectory line in the monitoring range area is within the monitoring range of the monitoring device corresponding to the captured image includes:

[0019] Recording the two ends of the abnormal trajectory line as abnormal monitoring points and obtaining the monitoring device corresponding to each abnormal monitoring;

[0020] Obtaining a monitoring angle between each of the abnormal monitoring points and the corresponding monitoring device, and judging whether the abnormal monitoring point of the abnormal trajectory line is within the monitoring range of the monitoring device corresponding to the captured image according to a comparison between the monitoring angle and the field of view of the device parameter information in the corresponding monitoring device;

[0021] If the two abnormal monitoring points of the abnormal trajectory line are within the monitoring range of the monitoring device corresponding to the captured image, there is no need to perform monitoring optimization on the monitoring range area.

[0022] On the other hand, a monitoring point optimization device integrating GIS technology and face recognition is provided, including a region determination module, a data acquisition module, an abnormality judgment module and a monitoring optimization module;

[0023] The area determination module is used to obtain map data of the monitoring area and device parameter information of each monitoring device in the monitoring area, and use GIS technology to perform regional gridding and device location marking processing on the monitoring area in turn according to all the device parameter information to obtain the monitoring range area of ​​each monitoring device;

[0024] The data acquisition module is used to acquire the monitoring data of each monitoring device in each monitoring range area, filter the data of the same person from all the monitoring data and sort the data by time to obtain the captured data of the same person; the monitoring data includes the captured image, the capturing time, the position of the person corresponding to the captured image and the transportation used by the person;

[0025] The abnormality judgment module is used to obtain the distance data and time difference between any two adjacent captured images and the normal speed of the vehicle used by the acquiring personnel from the captured data, calculate the trajectory speed according to the distance data and the time difference, and judge whether the trajectory line where the two adjacent captured images are located is an abnormal trajectory line according to the trajectory speed and the normal speed;

[0026] The monitoring optimization module is used to judge whether the abnormal trajectory line in the monitoring range area is within the monitoring range of the monitoring device corresponding to the captured image based on the trajectory line where the two adjacent captured images are located being an abnormal trajectory line; if not, construct virtual points on the abnormal trajectory line in the monitoring range area and obtain recommended construction values ​​to obtain a monitoring range map with optimized monitoring points.

[0027] Preferably, the monitoring optimization module includes a construction submodule, a judgment submodule, a calculation submodule and an optimization submodule;

[0028] The construction submodule is used to construct a plurality of virtual points on each abnormal trajectory line in the monitoring range area, and obtain a corresponding monitoring device in the grid of the monitoring range area where each virtual point is located to obtain a device data set;

[0029] The judgment submodule is used to obtain the angle between each monitoring device in the device data set and the corresponding virtual point, and judge whether the virtual point is within the field of view of the corresponding monitoring device according to the comparison between the angle and the field of view of the device parameter information in the corresponding monitoring device;

[0030] The calculation submodule is used for adding 1 to the construction recommendation value corresponding to the virtual point according to whether the virtual point is within the field of view of the corresponding monitoring device, until the virtual point traverses all the monitoring devices in the device data set to obtain the construction recommendation value corresponding to the virtual point;

[0031] The optimization submodule is used to use the GIS technology to overlay and display all abnormal trajectory lines and all corresponding virtual points and construction recommended values ​​on the monitoring range area to obtain a monitoring range map with optimized monitoring points.

[0032] Preferably, the data acquisition module is further used to acquire the monitoring data of each monitoring device in the monitoring range area by using a computer vision algorithm.

[0033] Preferably, the monitoring optimization module is also used to record the two ends of the abnormal trajectory line as abnormal monitoring points and obtain the monitoring equipment corresponding to each of the abnormal monitoring points; obtain the monitoring angle between each of the abnormal monitoring points and the corresponding monitoring equipment, and judge whether the abnormal monitoring point of the abnormal trajectory line is within the monitoring range of the monitoring equipment corresponding to the captured image based on the comparison of the monitoring angle with the field of view of the device parameter information in the corresponding monitoring equipment; if the two abnormal monitoring points of the abnormal trajectory line are within the monitoring range of the monitoring equipment corresponding to the captured image, there is no need to perform monitoring optimization on the monitoring range area.

[0034] In another aspect, a terminal device is provided, comprising a processor and a memory;

[0035] The memory is used to store program codes and transmit the program codes to the processor;

[0036] The processor is used to execute the monitoring point optimization method integrating GIS technology and face recognition according to the instructions in the program code.

[0037] The monitoring point optimization method, device and terminal integrating GIS technology and face recognition include obtaining map data of the monitoring area and equipment parameter information of each monitoring device in the monitoring area, using GIS technology to perform regional gridding and equipment position marking processing on the monitoring area in turn according to all equipment parameter information, and obtaining the monitoring range area of ​​each monitoring device; obtaining the monitoring data of each monitoring device in each monitoring range area, filtering the data of the same person from all monitoring data and sorting the data by time, and obtaining the captured data of the same person; the monitoring data includes the captured image, the captured time and the person corresponding to the captured image The position of the personnel and the means of transportation used by the personnel; the distance data and time difference of any two adjacent captured images and the normal speed of the means of transportation used by the personnel are obtained from the captured data, the trajectory speed is calculated according to the distance data and the time difference, and whether the trajectory line where the two adjacent captured images are located is an abnormal trajectory line is judged according to the trajectory speed and the normal speed; if the trajectory line where the two adjacent captured images are located is an abnormal trajectory line, it is judged whether the abnormal trajectory line in the monitoring range area is within the monitoring range of the monitoring device corresponding to the captured image; if not, virtual points are constructed on the abnormal trajectory line in the monitoring range area and the construction recommended values ​​are obtained to obtain a monitoring range map with optimized monitoring points.

[0038] It can be seen from the above technical scheme that the present application has the following advantages: the monitoring point optimization method integrating GIS technology and face recognition adopts GIS technology and face recognition technology to realize grid management of the monitoring area; and then provides data for abnormal trajectory line identification through the equipment parameter information and monitoring data of the monitoring equipment; analyzes the trajectory speed and abnormal trajectory line according to the monitoring data and snapshot data, identifies the monitoring blind spots in the monitoring area, constructs virtual points on the abnormal trajectory line and obtains the recommended construction values, obtains the monitoring range map after the precise monitoring point construction is optimized, significantly improves the coverage and monitoring efficiency of the monitoring network, effectively reduces the monitoring blind spots, and reduces the construction and operation costs of the monitoring system, and solves the technical problems that the traditional monitoring point layout relies on manual experience and on-site investigation, is inefficient, and it is difficult to ensure that the monitoring network can fully cover key areas, resulting in waste of resources.

[0039] The monitoring point optimization device integrating GIS technology and face recognition realizes accurate data analysis and intelligent optimization strategies through the area determination module, data acquisition module, abnormality judgment module and monitoring optimization module, and realizes the scientific layout of monitoring points. The monitoring point optimization device integrating GIS technology and face recognition will significantly improve the coverage and monitoring efficiency of the monitoring network, effectively reduce monitoring blind spots, and reduce the construction and operation costs of the monitoring system. At the same time, it will provide strong scientific decision-making support for city managers, strengthen the ability of public safety management, and ensure the safety and order of the city. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0041] Figure 1 This is a flowchart of the steps of the monitoring point optimization method integrating GIS technology and face recognition described in the embodiment of the present application;

[0042] Figure 2 This is a schematic diagram of the framework of the monitoring point optimization device integrating GIS technology and face recognition described in the embodiment of the present application;

[0043] Figure 3 A schematic diagram of a terminal device described in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] In the description of the embodiments of the present application, the terms "first" and "second" are used for descriptive purposes only and cannot 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 one or more of the features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0046] In the embodiments of the present application, 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 one; 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. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0047] Patent terms:

[0048] Video Image Analysis: The process of using computer image visual analysis technology to deeply analyze and process the content in video images.

[0049] Face Recognition: Quickly locate photos of the same person in a large database.

[0050] Face Clustering: refers to the process of classifying and grouping faces in different images, so that face images of the same person are clustered together, while face images of different people are separated.

[0051] GIS refers to Geographic Information System, which is a technical system for collecting, storing, querying, analyzing and displaying geographic spatial data.

[0052] Face capture: The process of capturing facial images of pedestrians through computer vision technology, and identifying and recording them.

[0053] Field of view (FOV): The maximum angle that a camera can capture.

[0054] Abnormal trajectory: The trajectory of pedestrians or vehicles in the monitoring area whose speed, direction, etc. do not conform to normal behavior patterns.

[0055] Virtual point creation: Simulate the setting of new camera positions on the map to evaluate the best installation location.

[0056] The embodiments of the present application provide a monitoring point optimization method, device and terminal that integrate GIS technology and face recognition, which solves the technical problems that the traditional monitoring point layout relies on manual experience and on-site investigation, is inefficient, and it is difficult to ensure that the monitoring network can fully cover key areas, resulting in waste of resources.

[0057] Embodiment 1:

[0058] Figure 1This is a flowchart of the steps of the monitoring point optimization method integrating GIS technology and face recognition described in an embodiment of the present application.

[0059] like Figure 1 As shown, the embodiment of the present application provides a monitoring point optimization method integrating GIS technology and face recognition, including the following steps:

[0060] S1. Obtain map data of the monitoring area and equipment parameter information of each monitoring device in the monitoring area, and use GIS technology to perform regional gridding and equipment location marking processing on the monitoring area in turn according to all equipment parameter information and map data to obtain the monitoring range area of ​​each monitoring device.

[0061] It should be noted that in step S1, the map data of the monitoring area is obtained and then the regional grid processing is performed to obtain the monitoring range area of ​​the monitoring range, so as to provide data for the subsequent optimization of the monitoring points. In this embodiment, the map data contains information such as geographic coordinates and geomorphic features. The equipment parameters include the height, focal length, field of view FOV, etc. of the monitoring equipment. Step S1 can be understood as first obtaining the map data of the monitoring area, and then using GIS technology to divide the area to be monitored into multiple grids. The area of ​​each grid can be set according to the actual situation, such as: 100 meters * 100 meters, 200 meters * 200 meters, 500 meters * 500 meters grid. Finally, the location of the existing monitoring equipment is marked on the map of the monitoring area, and the monitoring range of the corresponding monitoring equipment is drawn according to the equipment parameter information of the monitoring equipment, and the monitoring range area composed of the monitoring range of each monitoring equipment is obtained. The monitoring equipment can be a camera or other terminal with monitoring function. Among them, drawing the monitoring range of the corresponding monitoring equipment according to the equipment parameter information of the monitoring equipment can be understood as follows: the approximate monitoring range is known according to the equipment parameter information of the monitoring equipment, and finally an approximate monitoring range of the monitoring equipment point can be displayed on the map of the monitoring area, and then the modified data is imported into GIS and calculated to calculate a monitoring range of the plane map and display it on the map of the monitoring area.

[0062] S2. Obtain monitoring data from each monitoring device in each monitoring range area, filter the data of the same person from all monitoring data and sort the data by time to obtain the snapshot data of the same person; the monitoring data includes the snapshot image, the snapshot time, and the location of the person corresponding to the snapshot image and the transportation used by the person. In the monitoring range area, a computer vision algorithm is used to obtain the monitoring data of each monitoring device.

[0063] It should be noted that in step S2, the monitoring range area formed by the monitoring range of each monitoring device in the monitoring area is obtained according to step S1, and the monitoring data of each monitoring device is obtained in this monitoring range area. The data of the same person is filtered from the monitoring data and sorted by time to obtain the captured data of the same person. In this embodiment, the monitoring data can be obtained by the monitoring device using a computer vision algorithm to capture the face and pedestrian data within its monitoring range. And the means of transportation of pedestrians is identified or updated based on the captured images obtained in real time. Computer vision algorithm is a relatively mature face recognition technology in this field, and the specific content of the computer vision algorithm is not elaborated in detail in this embodiment. Snapshot is taken according to the constructed monitoring equipment. It can be understood that one is the data of captured faces that comes with the monitoring device, and the other is the face data extracted from the picture obtained by the monitoring device through the face algorithm.

[0064] In an embodiment of the present application, in a computer vision algorithm, if the similarity of persons identified in two captured images reaches 92%, they are considered to be the same person, that is, each captured record has a unique identifier of a person, which is used to determine whether it is the same person.

[0065] It should be noted that the monitoring data can be analyzed by randomly sampling 100 people's trajectory diagrams from each monitoring device, and the number of random samplings can be configured. After that, the monitoring data of the corresponding sampled personnel for one week is counted and sorted in chronological order to obtain the captured data for subsequent analysis.

[0066] S3. Obtain the distance data and time difference between any two adjacent captured images and the normal speed of the vehicle used by the person from the captured data, calculate the trajectory speed based on the distance data and the time difference, and determine whether the trajectory line where the two adjacent captured images are located is an abnormal trajectory line based on the trajectory speed and the normal speed. Among them, the trajectory speed is calculated by dividing the distance data and the time difference.

[0067] It should be noted that in step S3, the trajectory speed is calculated based on the distance data and time difference between any two adjacent captured images in the captured data obtained in step S2; then, the normal speed of the transportation vehicle used by the pedestrian in the captured data is compared with the trajectory speed to determine whether the trajectory line where the two adjacent captured images are located is an abnormal trajectory line. In this embodiment, the distance data of any two adjacent captured images refers to the record of the same person being captured twice in a row. It may be two records captured by the same monitoring device, or it may be records captured on two different monitoring devices. It can be understood as finding N capture records of this person, each record has a corresponding monitoring device point (the point has longitude and latitude) and capture time, and then sorted by time, and then began to calculate the time consumption of 1~2, then 2~3, 3~4 and so on until N-1~N.

[0068] In an embodiment of the present application, judging whether the trajectory line where two adjacent captured images are located is an abnormal trajectory line according to the trajectory speed and the normal speed includes: if the trajectory speed is greater than the normal speed, judging that the trajectory line where the two adjacent captured images are located is an abnormal trajectory line; if the trajectory speed is not greater than the normal speed, judging that the trajectory line where the two adjacent captured images are located is a normal trajectory line.

[0069] It should be noted that if the vehicle is a motorcycle, the normal speed limit of 60 kilometers per hour is used as the speed limit in the city. If the trajectory speed is greater than 60 kilometers per hour, the trajectory line where the two adjacent captured images are located is marked as an abnormal trajectory line.

[0070] S4. If the trajectory line where the two adjacent captured images are located is an abnormal trajectory line, determine whether the abnormal trajectory line in the monitoring range area is within the monitoring range of the monitoring device corresponding to the captured image; if not, construct virtual points on the abnormal trajectory line in the monitoring range area and obtain recommended construction values ​​to obtain a monitoring range map with optimized monitoring points.

[0071] It should be noted that in step S4, the presence of an abnormal trajectory line is determined according to step S3, and the abnormal trajectory layer is superimposed and displayed on the monitoring layer of the corresponding monitoring device in combination with GIS technology, and then it is first determined whether the abnormal trajectory line is within the monitoring range of the monitoring device. If not, a virtual point is constructed on the abnormal trajectory line and the recommended value of the virtual point is displayed. According to the recommended value of the virtual point, data is provided for the user to select the best installation monitoring device for the abnormal trajectory line. In this embodiment, the monitoring point optimization method integrating GIS technology and face recognition realizes accurate data analysis and intelligent optimization strategy through steps S1 to S4, and realizes the scientific layout of monitoring points. The monitoring point optimization method integrating GIS technology and face recognition will significantly improve the coverage and monitoring efficiency of the monitoring network, effectively reduce the monitoring blind spots, and reduce the construction and operation costs of the monitoring system. At the same time, it will provide strong scientific decision-making support for city managers, strengthen the ability of public safety management, and ensure the safety and order of the city.

[0072] In an embodiment of the present application, the user can directly observe which virtual points and recommended data are recommended for the installation of monitoring equipment through the monitoring range map obtained by the monitoring point optimization method integrating GIS technology and face recognition. Select the corresponding virtual point for construction. If the recommended virtual point is not satisfactory. The user can also construct the monitoring point of the abnormal trajectory line according to the logic of the monitoring system. Finally, after the construction of the monitoring point is completed, observe whether the abnormal trajectory line on the monitoring point disappears to verify whether the monitoring point is reasonably constructed.

[0073] The present application provides a monitoring point optimization method that integrates GIS technology and face recognition, including obtaining map data of the monitoring area and equipment parameter information of each monitoring device in the monitoring area, using GIS technology to perform regional gridding and equipment location marking processing on the monitoring area in turn according to all equipment parameter information, to obtain the monitoring range area of ​​each monitoring device; obtaining monitoring data of each monitoring device in each monitoring range area, filtering data of the same person from all monitoring data and sorting the data by time, to obtain captured data of the same person; the monitoring data includes captured images, capture time, and the position of the person corresponding to the captured image and the person’s location. means of transportation; obtaining the distance data and time difference of any two adjacent captured images and the normal speed of the means of transportation used by the personnel from the captured data, calculating the trajectory speed according to the distance data and the time difference, and judging whether the trajectory line where the two adjacent captured images are located is an abnormal trajectory line according to the trajectory speed and the normal speed; if the trajectory line where the two adjacent captured images are located is an abnormal trajectory line, judging whether the abnormal trajectory line in the monitoring range area is within the monitoring range of the monitoring device corresponding to the captured image; if not, constructing virtual points on the abnormal trajectory line in the monitoring range area and obtaining the recommended construction value, and obtaining a monitoring range map with optimized monitoring points. This monitoring point optimization method integrating GIS technology and face recognition uses GIS technology and face recognition technology to achieve grid management of the monitoring area; then provides data for abnormal trajectory line identification through equipment parameter information and monitoring data of the monitoring equipment; analyzes trajectory speed and abnormal trajectory line according to monitoring data and snapshot data, identifies monitoring blind spots in the monitoring area, constructs virtual points on the abnormal trajectory line and obtains recommended construction values, obtains a monitoring range map after accurate monitoring point construction optimization, significantly improves the coverage and monitoring efficiency of the monitoring network, effectively reduces monitoring blind spots, and reduces the construction and operation costs of the monitoring system; solves the technical problems that the traditional monitoring point layout relies on manual experience and on-site investigation, is inefficient, and is difficult to ensure that the monitoring network can fully cover key areas, resulting in waste of resources.

[0074] It should be noted that the monitoring point optimization method integrating GIS technology and face recognition uses GIS technology to realize the visualization of monitored virtual points and abnormal trajectory lines. In this embodiment, the monitoring area is optimized by integrating GIS technology and face recognition monitoring point optimization method, which enhances the monitoring coverage and monitoring efficiency of the monitoring area, reduces the monitoring blind spots of the monitoring area, improves the level of urban safety management, and provides support for scientific decision-making in urban management and optimizes resource allocation; simplifies the identification and decision-making process of monitoring points through a visual operation interface; improves the scientificity and accuracy of the monitoring point layout and reduces costs; and also improves the overall monitoring efficiency and coverage of the monitoring area.

[0075] In one embodiment of the present application, virtual point construction is performed on an abnormal trajectory line in a monitoring range area and recommended construction values ​​are obtained to obtain a monitoring range map with optimized monitoring points, including:

[0076] Construct multiple virtual points on each abnormal trajectory line in the monitoring range, obtain the corresponding monitoring equipment in the grid of the monitoring range where each virtual point is located, and obtain the equipment data set;

[0077] Obtain the angle between each monitoring device in the device data set and the corresponding virtual point, and determine whether the virtual point is within the field of view of the corresponding monitoring device based on the comparison between the angle and the field of view of the device parameter information in the corresponding monitoring device;

[0078] If the virtual point is within the field of view of the corresponding monitoring device, the construction recommended value of the corresponding virtual point is increased by 1 until the virtual point traverses all monitoring devices in the device data set to obtain the construction recommended value of the corresponding virtual point;

[0079] GIS technology is used to overlay and display all abnormal trajectory lines and all corresponding virtual points and recommended construction values ​​on the monitoring range area to obtain a monitoring range map with optimized monitoring points.

[0080] It should be noted that in step S3, all abnormal trajectory lines in the monitoring range area are obtained, and multiple virtual points (such as 12) are constructed for each abnormal trajectory line. Then, all monitoring devices in the grid where each virtual point is located are obtained according to the monitoring range area after grid division in step S1, and the equipment data set is obtained. Then, the angle between each monitoring device and the corresponding virtual point in the equipment data set is obtained, and the angle is compared with the field of view angle of the equipment parameter information of the corresponding monitoring device to determine whether the virtual point is within the field of view angle of the corresponding monitoring device. If so, the construction recommended value is increased by 1 until the virtual point traverses all monitoring devices in the equipment data set to obtain the construction recommended value of the corresponding virtual point, and the positions of all virtual points and their corresponding construction recommended values ​​are superimposed and displayed on the map of the monitoring range area using GIS technology to obtain a monitoring range map, so that the monitoring range map can display the points of the monitoring devices in the regional grid, the monitoring range of the monitoring devices, the abnormal trajectory lines, the recommended virtual points, and the construction recommended values ​​of the corresponding recommended points. Among them, the initial value of the construction recommended value is 0.

[0081] In the embodiment of the present application, obtaining the angle between each monitoring device and the corresponding virtual point in the device data set includes: assuming that the horizontal direction of the monitoring device is the positive direction of the x-axis, the direction vector of the monitoring device can be recorded as , the vector from the virtual point P to the monitoring device is recorded as , use the dot product formula to calculate the angle between the monitoring device and the corresponding virtual point If the angle If the angle is not greater than half of the field of view of the monitoring device FOV / 2, it means that the virtual point P is within the field of view of the corresponding monitoring device. If it is greater than half of the field of view FOV / 2 of the monitoring device, it means that the virtual point P is not within the field of view of the corresponding monitoring device.

[0082] It should be noted that the dot product formula is:

[0083]

[0084] In one embodiment of the present application, determining whether an abnormal trajectory line in a monitoring range area is within a monitoring range of a monitoring device corresponding to a captured image includes:

[0085] Record the two ends of the abnormal trajectory line as abnormal monitoring points and obtain the monitoring equipment corresponding to each abnormal monitoring;

[0086] Obtain the monitoring angle between each abnormal monitoring point and the corresponding monitoring device, and compare the monitoring angle with the field of view of the device parameter information in the corresponding monitoring device to determine whether the abnormal monitoring point of the abnormal trajectory line is within the monitoring range of the monitoring device corresponding to the captured image;

[0087] If the two abnormal monitoring points of the abnormal trajectory line are within the monitoring range of the monitoring device corresponding to the captured image, there is no need to optimize the monitoring range area.

[0088] It should be noted that the monitoring angle between each abnormal monitoring point and the corresponding monitoring device can be calculated using the above-mentioned dot product formula. Judging whether the abnormal trajectory line is within the monitoring range of the monitoring device corresponding to the captured image can be understood as follows: according to the monitoring angle not greater than half of the field of view FOV / 2 of the monitoring device, the abnormal monitoring point of the abnormal trajectory line is within the monitoring range of the monitoring device corresponding to the captured image. If the monitoring angle is greater than half of the field of view FOV / 2 of the monitoring device, it means that the abnormal monitoring point of the abnormal trajectory line is not within the monitoring range of the monitoring device corresponding to the captured image.

[0089] Embodiment 2:

[0090] Figure 2 This is a schematic diagram of the framework of the monitoring point optimization device that integrates GIS technology and face recognition as described in an embodiment of the present application.

[0091] like Figure 2 As shown, the embodiment of the present application provides a monitoring point optimization device integrating GIS technology and face recognition, including an area determination module 10, a data acquisition module 20, an abnormality judgment module 30 and a monitoring optimization module 40;

[0092] The area determination module 10 is used to obtain the map data of the monitoring area and the equipment parameter information of each monitoring device in the monitoring area, and use GIS technology to perform regional gridding and equipment location marking processing on the monitoring area in turn according to all the equipment parameter information to obtain the monitoring range area of ​​each monitoring device;

[0093] The data acquisition module 20 is used to acquire the monitoring data of each monitoring device in each monitoring range area, filter the data of the same person from all the monitoring data and sort the data by time to obtain the captured data of the same person; the monitoring data includes the captured image, the capturing time, the position of the person corresponding to the captured image, and the transportation used by the person;

[0094] The abnormality judgment module 30 is used to obtain the distance data and time difference between any two adjacent captured images and the normal speed of the vehicle used by the person from the captured data, calculate the trajectory speed according to the distance data and the time difference, and judge whether the trajectory line where the two adjacent captured images are located is an abnormal trajectory line according to the trajectory speed and the normal speed;

[0095] The monitoring optimization module 40 is used to judge whether the abnormal trajectory line in the monitoring range area is within the monitoring range of the monitoring device corresponding to the captured image based on the trajectory line of two adjacent captured images being an abnormal trajectory line; if not, construct virtual points on the abnormal trajectory line in the monitoring range area and obtain recommended construction values ​​to obtain a monitoring range map with optimized monitoring points.

[0096] It should be noted that the module content of the monitoring point optimization device integrating GIS technology and face recognition corresponds to the content of the steps in the method of Example 1. Example 1 has elaborated on the steps of the monitoring point optimization method integrating GIS technology and face recognition. In this embodiment, the module content of the monitoring point optimization device integrating GIS technology and face recognition is no longer repeated. The monitoring point optimization device integrating GIS technology and face recognition realizes accurate data analysis and intelligent optimization strategy through the area determination module 10, the data acquisition module 20, the abnormality judgment module 30 and the monitoring optimization module 40, and realizes the scientific layout of monitoring points. The monitoring point optimization device integrating GIS technology and face recognition will significantly improve the coverage and monitoring efficiency of the monitoring network, effectively reduce the monitoring blind spots, and reduce the construction and operation costs of the monitoring system. At the same time, it will provide strong scientific decision-making support for urban managers, strengthen the ability of public safety management, and ensure the safety and order of the city.

[0097] In the embodiment of the present application, the monitoring optimization module 40 includes a construction submodule, a judgment submodule, a calculation submodule and an optimization submodule;

[0098] The construction submodule is used to construct multiple virtual points on each abnormal trajectory line in the monitoring range area, obtain the corresponding monitoring equipment in the grid of the monitoring range area where each virtual point is located, and obtain the equipment data set;

[0099] The judgment submodule is used to obtain the angle between each monitoring device in the device data set and the corresponding virtual point, and judge whether the virtual point is within the field of view of the corresponding monitoring device according to the comparison between the angle and the field of view of the device parameter information in the corresponding monitoring device;

[0100] The calculation submodule is used to increase the construction recommendation value of the corresponding virtual point by 1 according to the virtual point being within the field of view of the corresponding monitoring device, until the virtual point traverses all monitoring devices in the device data set to obtain the construction recommendation value of the corresponding virtual point;

[0101] The optimization submodule is used to use GIS technology to overlay and display all abnormal trajectory lines and all corresponding virtual points and construction recommended values ​​on the monitoring range area to obtain a monitoring range map with optimized monitoring points.

[0102] In the embodiment of the present application, the data acquisition module 10 is also used to acquire the monitoring data of each monitoring device in the monitoring range area using a computer vision algorithm.

[0103] In an embodiment of the present application, the monitoring optimization module 40 is also used to record the two ends of the abnormal trajectory line as abnormal monitoring points and obtain the monitoring equipment corresponding to each abnormal monitoring point; obtain the monitoring angle between each abnormal monitoring point and the corresponding monitoring equipment, and judge whether the abnormal monitoring point of the abnormal trajectory line is within the monitoring range of the monitoring equipment corresponding to the captured image based on the comparison of the monitoring angle with the field of view of the equipment parameter information in the corresponding monitoring equipment; if the two abnormal monitoring points of the abnormal trajectory line are within the monitoring range of the monitoring equipment corresponding to the captured image, there is no need to perform monitoring optimization on the monitoring range area.

[0104] Embodiment three:

[0105] Figure 3 A schematic diagram of a terminal device described in an embodiment of the present application.

[0106] like Figure 3 As shown, an embodiment of the present application provides a terminal device, including a processor and a memory;

[0107] A memory, used for storing program codes and transmitting the program codes to a processor;

[0108] The processor is used to execute the above-mentioned monitoring point optimization method integrating GIS technology and face recognition according to the instructions in the program code.

[0109] It should be noted that the processor is used to execute the steps in the above-mentioned embodiment of the monitoring point optimization method integrating GIS technology and face recognition according to the instructions in the program code. Alternatively, the processor implements the functions of each module / unit in the above-mentioned system / device embodiments when executing the computer program.

[0110] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in a memory and executed by a processor to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing a specific function, and the instruction segments are used to describe the execution process of the computer program in a terminal device.

[0111] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that this does not constitute a limitation on the terminal device, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0112] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (dSIC), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0113] The memory may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The memory may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device. Furthermore, the memory may include both an internal storage unit of the terminal device and an external storage device. The memory is used to store computer programs and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is to be output.

[0114] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0115] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0116] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0118] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0119] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A monitoring point optimization method integrating GIS technology and face recognition, characterized in that: The following steps are involved: Obtain map data of the monitoring area and equipment parameter information of each monitoring device in the monitoring area, and use GIS technology to perform regional gridding and equipment location marking processing on the monitoring area in turn according to all the equipment parameter information to obtain the monitoring range area of ​​each monitoring device; Acquire monitoring data of each monitoring device in each monitoring range area, filter data of the same person from all the monitoring data and sort the data by time to obtain snapshot data of the same person; the monitoring data includes a snapshot image, a snapshot time, and a position of the person corresponding to the snapshot image and a means of transportation used by the person; Obtaining the distance data and time difference between any two adjacent captured images and the normal speed of the vehicle used by the acquiring personnel from the captured data, calculating the trajectory speed according to the distance data and the time difference, and judging whether the trajectory line where the two adjacent captured images are located is an abnormal trajectory line according to the trajectory speed and the normal speed; If the trajectory lines of the two adjacent captured images are abnormal trajectory lines, determining whether the abnormal trajectory line in the monitoring range area is within the monitoring range of the monitoring device corresponding to the captured images; If not, a virtual point is constructed on the abnormal trajectory line in the monitoring range area and a recommended construction value is obtained to obtain a monitoring range map with optimized monitoring points.

2. The monitoring point optimization method integrating GIS technology and face recognition according to claim 1 is characterized in that: Constructing virtual points on the abnormal trajectory line in the monitoring range area and obtaining recommended construction values, and obtaining a monitoring range map with optimized monitoring points includes: Constructing a plurality of virtual points on each abnormal trajectory line in the monitoring range area, acquiring corresponding monitoring equipment in the grid of the monitoring range area where each virtual point is located, and obtaining a device data set; Obtaining an angle between each monitoring device in the device data set and the corresponding virtual point, and judging whether the virtual point is within the field of view of the corresponding monitoring device according to a comparison between the angle and the field of view of the device parameter information in the corresponding monitoring device; If the virtual point is within the field of view of the corresponding monitoring device, the construction recommendation value corresponding to the virtual point is increased by 1 until the virtual point traverses all the monitoring devices in the device data set to obtain the construction recommendation value corresponding to the virtual point; The GIS technology is used to overlay and display all abnormal trajectory lines and all corresponding virtual points and construction recommended values ​​on the monitoring range area to obtain a monitoring range map with optimized monitoring points.

3. The monitoring point optimization method integrating GIS technology and face recognition according to claim 1 is characterized in that: This includes using a computer vision algorithm to acquire monitoring data of each of the monitoring devices in the monitoring range area.

4. The monitoring point optimization method integrating GIS technology and face recognition according to claim 1 is characterized in that: Calculating the trajectory speed according to the distance data and the time difference includes: calculating the trajectory speed by dividing the distance data by the time difference.

5. The monitoring point optimization method integrating GIS technology and face recognition according to claim 1 is characterized in that: The step of judging whether the abnormal trajectory line in the monitoring range area is within the monitoring range of the monitoring device corresponding to the captured image includes: Recording the two ends of the abnormal trajectory line as abnormal monitoring points and obtaining the monitoring device corresponding to each abnormal monitoring; Obtaining a monitoring angle between each of the abnormal monitoring points and the corresponding monitoring device, and judging whether the abnormal monitoring point of the abnormal trajectory line is within the monitoring range of the monitoring device corresponding to the captured image according to a comparison between the monitoring angle and the field of view of the device parameter information in the corresponding monitoring device; If the two abnormal monitoring points of the abnormal trajectory line are within the monitoring range of the monitoring device corresponding to the captured image, there is no need to perform monitoring optimization on the monitoring range area.

6. A monitoring point optimization device integrating GIS technology and face recognition, characterized in that: It includes area determination module, data acquisition module, abnormality judgment module and monitoring optimization module; The area determination module is used to obtain map data of the monitoring area and device parameter information of each monitoring device in the monitoring area, and use GIS technology to perform regional gridding and device location marking processing on the monitoring area in turn according to all the device parameter information to obtain the monitoring range area of ​​each monitoring device; The data acquisition module is used to acquire the monitoring data of each monitoring device in each monitoring range area, filter the data of the same person from all the monitoring data and sort the data by time to obtain the captured data of the same person; the monitoring data includes the captured image, the capturing time, the position of the person corresponding to the captured image and the transportation used by the person; The abnormality judgment module is used to obtain the distance data and time difference between any two adjacent captured images and the normal speed of the vehicle used by the acquiring personnel from the captured data, calculate the trajectory speed according to the distance data and the time difference, and judge whether the trajectory line where the two adjacent captured images are located is an abnormal trajectory line according to the trajectory speed and the normal speed; The monitoring optimization module is used to determine whether the abnormal trajectory line in the monitoring range area is within the monitoring range of the monitoring device corresponding to the captured image according to the trajectory line where the two adjacent captured images are located being the abnormal trajectory line; If not, a virtual point is constructed on the abnormal trajectory line in the monitoring range area and a recommended construction value is obtained to obtain a monitoring range map with optimized monitoring points.

7. The monitoring point optimization device integrating GIS technology and face recognition according to claim 6 is characterized in that: The monitoring optimization module includes a construction submodule, a judgment submodule, a calculation submodule and an optimization submodule; The construction submodule is used to construct a plurality of virtual points on each abnormal trajectory line in the monitoring range area, and obtain the corresponding monitoring equipment in the grid of the monitoring range area where each virtual point is located to obtain the equipment data set; The judgment submodule is used to obtain the angle between each monitoring device in the device data set and the corresponding virtual point, and judge whether the virtual point is within the field of view of the corresponding monitoring device according to the comparison between the angle and the field of view of the device parameter information in the corresponding monitoring device; The calculation submodule is used for adding 1 to the construction recommendation value corresponding to the virtual point according to whether the virtual point is within the field of view of the corresponding monitoring device, until the virtual point traverses all the monitoring devices in the device data set to obtain the construction recommendation value corresponding to the virtual point; The optimization submodule is used to use the GIS technology to overlay and display all abnormal trajectory lines and all corresponding virtual points and construction recommended values ​​on the monitoring range area to obtain a monitoring range map with optimized monitoring points.

8. The monitoring point optimization device integrating GIS technology and face recognition according to claim 6 is characterized in that: The data acquisition module is also used to acquire the monitoring data of each monitoring device in the monitoring range area using a computer vision algorithm.

9. The monitoring point optimization device integrating GIS technology and face recognition according to claim 6 is characterized in that: The monitoring optimization module is also used to record the two ends of the abnormal trajectory line as abnormal monitoring points and obtain the monitoring equipment corresponding to each of the abnormal monitoring points; obtain the monitoring angle between each of the abnormal monitoring points and the corresponding monitoring equipment, and judge whether the abnormal monitoring point of the abnormal trajectory line is within the monitoring range of the monitoring equipment corresponding to the captured image based on the comparison of the monitoring angle with the field of view of the device parameter information in the corresponding monitoring equipment; if the two abnormal monitoring points of the abnormal trajectory line are within the monitoring range of the monitoring equipment corresponding to the captured image, there is no need to perform monitoring optimization on the monitoring range area.

10. A terminal device, characterized in that: including a processor and a memory; The memory is used to store program codes and transmit the program codes to the processor; The processor is used to execute the monitoring point optimization method integrating GIS technology and face recognition as described in any one of claims 1 to 5 according to the instructions in the program code.

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