Public place security video monitoring management system and method

Abstract

The invention relates to the technical field of monitoring video management, in particular to a public place security and protection video monitoring management system and method, and the method comprises the steps: obtaining video data of all cameras in a public place monitoring region, and screening out candidate video streams for subsequent security and protection video monitoring management from the obtained video data; calculating the similarity between the video frame of the monitoring area and the candidate video stream, and carrying out image matching based on a calculation result; and according to the constructed public place monitoring area matching image set, abnormal behavior detection is realized through a cross validation algorithm. According to the method, the candidate video streams are automatically screened out by calculating the similarity between the video frames and the visual angle difference of the camera, so that the processing burden of irrelevant video streams is reduced; by means of multi-camera data streams and scene layout information, event coverage in a monitoring area is ensured to be complete through cross validation, potential abnormal behaviors and potential safety hazards can be effectively identified in combination with an anomaly detection algorithm, and the identification precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring video management, and in particular to a public place security video monitoring management system and method. Background Art

[0002] The public security video surveillance system is designed to achieve real-time monitoring and post-management of people, vehicles, and objects. It ensures the safety of public places through comprehensive means such as efficient video surveillance equipment, data transmission network, intelligent analysis technology, and management platform. However, at this stage, it faces the following problems: With the widespread deployment of surveillance equipment, a large amount of video data is generated. Manual screening and analysis is not only time-consuming, but may also miss key information. Due to the lack of effective multi-view verification and scene layout correlation analysis, existing systems are prone to false positives or false negatives in complex scenarios; Current technologies lack effective image matching methods when dealing with dynamic targets (such as fast-moving people or vehicles) and complex backgrounds. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] To solve the above technical problems, the present invention provides the following technical solutions: a public place security video surveillance management method, comprising the following steps: Obtain video data from all cameras in the surveillance area of ​​a public place, and filter out candidate video streams for subsequent security video surveillance management from the acquired video data, specifically: The video data of the monitoring area is acquired by deploying multiple cameras, and a deviation factor is obtained according to the difference between the video frames of the cameras in the monitoring area. The deviation factor is used to determine the similarity between the video streams, including determining the similarity between the images by the structural similarity between each pair of video frames, and determining the geometric similarity between the video frames by the difference in viewing angles between the cameras. At the same time, a deviation factor threshold is set to screen the candidate video streams; The video frames corresponding to the surveillance area video and the video frames corresponding to the candidate video stream are similarly calculated, and image matching is performed according to the calculation results. Based on the image matching results, the matching image of the surveillance area of ​​the public place is obtained; Furthermore, for the constructed public place surveillance area matching image collection, multi-camera data streams are used and cross-validated in combination with camera layout information, while anomaly detection algorithms are used to detect potential abnormal behaviors and safety hazards.

[0005] As a preferred solution of the public place security video surveillance management method described in the present invention, each pair of video frames as well as The structural similarity between them determines the similarity between images, as follows: in, Respectively as well as The mean of Respectively as well as The variance of express and The covariance between Represents a correction item, Represents the calculated structural similarity between video frames, which is used to determine the similarity between images.

[0006] As a preferred solution of the public place security video surveillance management method of the present invention, the geometric similarity between video frames determined by the viewing angle difference between the cameras is specifically as follows: in, Respectively represent cameras and The viewing angle vector, represents the dot product operation, They represent the modulus corresponding to the viewing angle vector, Represents the difference in viewing angle between two cameras, which is the geometric similarity between the video frames corresponding to the cameras.

[0007] As a preferred solution of the public place security video surveillance management method of the present invention, the setting of the deviation factor threshold to screen the candidate video streams is specifically as follows: According to the determined image similarity and geometric similarity, a deviation factor is constructed, which is: in, Represents the calculated structural similarity between video frames. To determine the similarity between images, represents the difference in viewing angle between the two cameras, and is the geometric similarity between the video frames corresponding to the cameras. Represents the weight coefficient, which is set by the implementer according to the actual application scenario. Indicates the calculated deviation factor, which is used to implement video stream selection, specifically: Setting the Deviation Factor Threshold , If the calculated deviation factor meets the deviation factor threshold, the formula , which means that at the same moment, the two frames of video corresponding to the camera have similar contents. At this time, the video data collected by the camera is used as a candidate video stream for subsequent video surveillance management; If the calculated deviation factor meets the deviation factor threshold, the formula , indicating that at the same moment, the content of the two frames of video corresponding to the camera deviates. At this time, the video data collected by the camera is marked as an invalid video stream.

[0008] As a preferred solution of the public place security video surveillance management method described in the present invention, the similarity between the video stream of the monitoring area and the candidate video stream is calculated as follows: Arbitrarily select the video data collected by the camera in the monitoring area as the video data in the monitoring area, and construct the video data in the monitoring area into a video frame form, then, in, Indicates a point in time, Indicates time The monitoring frame at Indicates time The monitoring frame at A set of video frames representing a monitored area; The screened candidate videos are used to construct a candidate video stream frame set, then we have: ,in, Indicates candidate video streams, Indicates a point in time, a set of video frames representing a candidate video stream; The two video frames are converted into corresponding feature vectors through the same convolutional neural network. as well as , respectively represent monitoring frames The feature vector and candidate video frame The eigenvector of According to the transformed feature vector, the Euclidean distance between the two is calculated, then, in, Represents the dimension sequence number of the feature vector, represents the upper limit of the dimension of the feature vector, , Respectively represent The feature vectors corresponding to the two videos in dimension, Represents the calculated Euclidean distance, which is used to determine the degree of match between two frames.

[0009] As a preferred solution of the public place security video surveillance management method described in the present invention, the image matching according to the calculation results is specifically as follows: Set the matching degree judgment threshold , If the Euclidean distance between the two video frames and the threshold value comparison satisfies the formula , indicating that the two video frames match; If the Euclidean distance between the two video frames and the threshold value comparison satisfies the formula , indicating that the two video frames do not match; Iterate over all video frame pairs , and the results that satisfy the matching degree form a set of matching images, then, in, Represents the formed matching image set of public place monitoring area, represents the calculated Euclidean distance, Indicates the matching degree judgment threshold, Represents a video frame pair.

[0010] As a preferred solution of the public place security video surveillance management method described in the present invention, the cross-validation is specifically as follows: Build a scene layout model based on camera deployment information , including, camera position , Camera View And scene partition information ; Based on the historical abnormal behavior database, set the abnormal behavior label set ; Before abnormal behavior detection, the constructed scene layout is cross-validated, specifically: in, Indicates Camera and The distance between the cameras, Indicates the distance threshold between the two cameras. Indicates scene partition information. It is used to limit the division of scene partitions to not exceed the scene partition range information. Indicates the viewing angle of the camera. Indicates the minimum number of cameras covered. Indicates the indicator function. When the value is 1, it indicates Covered by the camera's view, Indicates the total number of deployed cameras, Indicates the total number of scene partitions.

[0011] As a preferred solution of the public place security video surveillance management method described in the present invention, the use of anomaly detection algorithms to detect potential abnormal behaviors and safety hazards is specifically as follows: If the behavior feature vector With behavior tag collection The feature comparison results in satisfy the formula When , it means that the behavior feature extracted from the video data of the current monitoring area is consistent with the label in the constructed abnormal behavior label set, and the current behavior feature is abnormal behavior, where, represents the structural similarity between two features. Indicates the set structural similarity threshold; If the behavior feature vector With behavior tag collection The feature comparison results in satisfy the formula , it means that the behavior feature extracted from the video data of the current monitoring area does not match the label in the constructed abnormal behavior label set, and the current behavior feature is not an abnormal behavior.

[0012] A public place security video surveillance management system, including a video data acquisition and processing module, an image matching module and an abnormal behavior recognition module. The video data acquisition processing module is used to acquire camera video data within the monitoring area of ​​the public place and filter out candidate video streams from the video data; The image matching module performs image matching on the video frame corresponding to the surveillance area video and the candidate video stream, and acquires the matching image of the surveillance area of ​​the public place based on the image matching result; The abnormal behavior recognition module checks for potential abnormal behaviors and safety hazards based on the constructed public place monitoring area matching image set.

[0013] Beneficial effects of the present invention: The present invention automatically screens out candidate video streams by calculating the similarity between video frames and the difference in camera viewing angles, reducing the processing burden of irrelevant video streams, thereby greatly improving the efficiency of the security video surveillance system; With the help of multi-camera data streams and scene layout information, cross-validation is used to ensure complete coverage of events in the monitoring area. Combined with anomaly detection algorithms, potential abnormal behaviors and safety hazards can be effectively identified, improving recognition accuracy. The deep learning-based feature extraction method can adapt to complex and dynamically changing monitoring environments and accurately match images even when multiple targets overlap or the lighting changes. By constructing a matching image set and using an efficient similarity measurement method (such as Euclidean distance), the present invention significantly improves the speed and accuracy of image matching and provides technical support for real-time monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 The present invention is a schematic diagram of the overall method steps of a public place security video surveillance management method. DETAILED DESCRIPTION

[0015] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0017] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0018] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0019] Meanwhile, in the description of the present invention, it should be noted that the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0020] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0021] Example 1: Reference Figure 1 , as an embodiment of the present invention, provides a public place security video surveillance management method, comprising the following steps, S1: Obtain video data from all cameras in the surveillance area of ​​a public place, and filter out candidate video streams for subsequent security video surveillance management from the acquired video data.

[0022] Specifically, the method of screening out candidate video streams for subsequent security video surveillance management from the acquired video data is by deploying multiple cameras to acquire the video data of the monitoring area, and deriving a deviation factor based on the difference between the camera video frames in the monitoring area. At the same time, a deviation factor threshold is set, and the calculated deviation factor is compared with the deviation factor threshold. The video streams whose deviation factors are lower than the deviation factor threshold are taken as candidate video streams, thereby completing the screening of the candidate video streams.

[0023] Furthermore, the deviation factor is used to determine the similarity between video streams, and is specifically calculated as follows: To set the monitoring area: cameras, each camera ,in, Represents the serial number of the camera and satisfies the formula , each camera captures a video frame at the same point in time , Indicates Cameras in The image frame captured at the moment; In order to synchronize the video streams of different cameras in the monitoring area, the frames of each video stream are time-aligned, then, , and satisfies the formula , indicating that the synchronization between the two cameras is completed; By calculating each pair of video frames as well as The structural similarity between them determines the similarity between images, which is calculated as follows: in, Respectively as well as The mean of Respectively as well as The variance of express and The covariance between It represents the correction term to prevent the denominator from being zero. The specific value is set by the implementer according to the actual application scenario. Represents the calculated structural similarity between video frames, to determine the similarity between images; The geometric similarity between video frames is determined by calculating the difference in viewing angles between cameras. The specific calculation is as follows: in, Respectively represent cameras and The viewing angle vector, represents the dot product operation, They represent the modulus corresponding to the viewing angle vector, Represents the difference in viewing angle between two cameras, which is the geometric similarity between the video frames corresponding to the cameras; According to the determined image similarity and geometric similarity, a deviation factor is constructed, which is: in, Represents the calculated structural similarity between video frames. To determine the similarity between images, represents the difference in viewing angle between the two cameras, and is the geometric similarity between the video frames corresponding to the cameras. Represents the weight coefficient, which is set by the implementer according to the actual application scenario. Indicates the calculated deviation factor, which is used to implement video stream selection, specifically: Setting the Deviation Factor Threshold , If the calculated deviation factor meets the deviation factor threshold, the formula , which means that at the same moment, the two frames of video corresponding to the camera have similar contents. At this time, the video data collected by the camera is used as a candidate video stream for subsequent video surveillance management; If the calculated deviation factor meets the deviation factor threshold, the formula , indicating that at the same moment, the content of the two frames of video corresponding to the camera deviates. At this time, the video data collected by the camera is marked as an invalid video stream.

[0024] S2: Calculate the similarity between the video frame of the monitoring area and the candidate video stream, and perform image matching based on the calculation results.

[0025] Specifically, the similarity between the surveillance area video frame and the candidate video stream is calculated by performing similarity calculation on the video frame corresponding to the surveillance area video and the video frame corresponding to the candidate video stream, and performing image matching according to the calculation result, and completing the acquisition of the matching image of the surveillance area of ​​the public place based on the image matching result, as follows: Arbitrarily select the video data collected by the camera in the monitoring area as the video data in the monitoring area, and construct the video data in the monitoring area into a video frame form, then, in, Indicates a point in time, Indicates time The monitoring frame at Indicates time The monitoring frame at A set of video frames representing a monitored area; The screened candidate videos are used to construct a candidate video stream frame set, then we have: ,in, Indicates candidate video streams, Indicates a point in time, a set of video frames representing a candidate video stream; The two video frames are converted into corresponding feature vectors through the same convolutional neural network. as well as , respectively represent monitoring frames The feature vector and candidate video frame The eigenvector of According to the transformed feature vector, the Euclidean distance between the two is calculated, then, in, Represents the dimension sequence number of the feature vector, represents the upper limit of the dimension of the feature vector, , Respectively represent The feature vectors corresponding to the two videos in dimension, Represents the calculated Euclidean distance, which is used to determine the degree of match between two frames. Specifically: Set the matching degree judgment threshold , If the Euclidean distance between the two video frames and the threshold value comparison satisfies the formula , indicating that the two video frames match; If the Euclidean distance between the two video frames and the threshold value comparison satisfies the formula , indicating that the two video frames do not match; Iterate over all video frame pairs , and the results that satisfy the matching degree form a set of matching images, then, in, Represents the formed matching image set of public place monitoring area, represents the calculated Euclidean distance, Indicates the matching degree judgment threshold, Represents a video frame pair.

[0026] S3: Based on the constructed public place monitoring area matching image set, abnormal behavior detection is achieved through a cross-validation algorithm.

[0027] Specifically, the detection of abnormal behavior is to match the image set of the constructed public place monitoring area, use multi-camera data streams, and combine the camera layout information for cross-validation, and use anomaly detection algorithms to detect potential abnormal behaviors and safety hazards. The specific implementation is as follows: Build a scene layout model based on camera deployment information , including, camera position , Camera View And scene partition information ; Based on the historical abnormal behavior database, set the abnormal behavior label set .

[0028] Before abnormal behavior detection, the constructed scene layout is cross-validated, specifically: in, Indicates Camera and The distance between the cameras, Indicates the distance threshold between the two cameras, which is set by the implementer according to the actual application scenario. Indicates scene partition information. It is used to limit the division of scene partitions to not exceed the scene partition range information. Indicates the viewing angle of the camera. Indicates the minimum number of cameras covered. Indicates the indicator function. When the value is 1, it indicates Covered by camera view.

[0029] It should be noted that for the cross-validation of the scene layout, including the verification of distance, scene information and camera field of view coverage, if all are passed, it means that the constructed scene layout model is complete. Otherwise, it means that the constructed scene layout model is not complete. The camera position information is readjusted until the scene model is constructed, and abnormal behavior detection is performed based on the constructed scene model.

[0030] Furthermore, the abnormal behavior detection is to extract the behavior feature vector of each frame using the deep learning algorithm for the video data of the monitoring area under the scene model. , and compare the extracted behavior feature vector with the set abnormal behavior label set The features in are compared, specifically: If the behavior feature vector With behavior tag collection The feature comparison results in satisfy the formula When , it means that the behavior feature extracted from the video data of the current monitoring area is consistent with the label in the constructed abnormal behavior label set, and the current behavior feature is abnormal behavior; If the behavior feature vector With behavior tag collection The feature comparison results in satisfy the formula , it means that the behavior feature extracted from the video data of the current monitoring area does not match the label in the constructed abnormal behavior label set, and the current behavior feature is not an abnormal behavior.

[0031] A public place security video surveillance management system, including a video data acquisition and processing module, an image matching module and an abnormal behavior recognition module. The video data acquisition processing module is used to acquire camera video data within the monitoring area of ​​the public place and filter out candidate video streams from the video data; The image matching module performs image matching on the video frame corresponding to the surveillance area video and the candidate video stream, and acquires the matching image of the surveillance area of ​​the public place based on the image matching result; The abnormal behavior identification module checks for potential abnormal behaviors and safety hazards based on the constructed public place monitoring area matching image set.

[0032] Furthermore, if the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the 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 enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0033] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0034] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0035] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those features that are not relevant to implementing the invention).

[0036] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0037] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for managing security video surveillance in public places, characterized in that: The following steps are included to obtain video data of all cameras in the monitoring area of ​​a public place, and to filter out candidate video streams for subsequent security video monitoring management from the obtained video data, specifically: The video data of the monitoring area is acquired by deploying multiple cameras, and a deviation factor is obtained according to the difference between the video frames of the cameras in the monitoring area. The deviation factor is used to determine the similarity between the video streams, including determining the similarity between the images by the structural similarity between each pair of video frames, and determining the geometric similarity between the video frames by the difference in viewing angles between the cameras. At the same time, a deviation factor threshold is set to screen the candidate video streams; The video frames corresponding to the surveillance area video and the video frames corresponding to the candidate video stream are similarly calculated, and image matching is performed according to the calculation results. Based on the image matching results, the matching image of the surveillance area of ​​the public place is obtained; Furthermore, for the constructed public place surveillance area matching image collection, multi-camera data streams are used and cross-validated in combination with camera layout information, while anomaly detection algorithms are used to detect potential abnormal behaviors and safety hazards.

2. A public place security video surveillance management method as claimed in claim 1, characterized in that: Each pair of video frames as well as The structural similarity between them determines the similarity between images, as follows: in, Respectively as well as The mean of Respectively as well as The variance of express and The covariance between Represents a correction term, Represents the calculated structural similarity between video frames, which is used to determine the similarity between images.

3. A public place security video surveillance management method as claimed in claim 2, characterized in that: The difference in viewing angles between the cameras determines the geometric similarity between video frames as follows: in, Respectively represent cameras and The viewing angle vector, represents the dot product operation, They represent the modulus corresponding to the viewing angle vector, Represents the difference in viewing angle between two cameras, which is the geometric similarity between the video frames corresponding to the cameras.

4. A public place security video surveillance management method as claimed in claim 3, characterized in that: The setting of the deviation factor threshold to screen the candidate video streams is specifically as follows: According to the determined image similarity and geometric similarity, a deviation factor is constructed, which is: in, Represents the calculated structural similarity between video frames. To determine the similarity between images, represents the difference in viewing angle between the two cameras, and is the geometric similarity between the video frames corresponding to the cameras. Represents the weight coefficient, which is set by the implementer according to the actual application scenario. Indicates the calculated deviation factor, which is used to implement video stream selection, specifically: Setting the Deviation Factor Threshold , If the calculated deviation factor meets the deviation factor threshold, the formula , which means that at the same moment, the two frames of video corresponding to the camera have similar contents. At this time, the video data collected by the camera is used as a candidate video stream for subsequent video surveillance management; If the calculated deviation factor meets the deviation factor threshold, the formula , indicating that at the same moment, the content of the two frames of video corresponding to the camera deviates. At this time, the video data collected by the camera is marked as an invalid video stream.

5. A public place security video surveillance management method as claimed in claim 4, characterized in that: The similarity between the video stream of the monitoring area and the candidate video stream is calculated as follows: Arbitrarily select the video data collected by the camera in the monitoring area as the video data in the monitoring area, and construct the video data in the monitoring area into a video frame form, then, in, Indicates a point in time, Indicates time The monitoring frame at Indicates time The monitoring frame at A set of video frames representing a monitored area; The screened candidate videos are used to construct a candidate video stream frame set, then we have: ,in, Indicates candidate video streams, Indicates a point in time, a set of video frames representing a candidate video stream; The two video frames are converted into corresponding feature vectors through the same convolutional neural network. as well as , respectively represent monitoring frames The feature vector and candidate video frame The eigenvector of According to the transformed feature vector, the Euclidean distance between the two is calculated, then, in, Represents the dimension sequence number of the feature vector, represents the upper limit of the dimension of the feature vector, , Respectively represent The feature vectors corresponding to the two videos in dimension, Represents the calculated Euclidean distance, which is used to determine the degree of match between two frames.

6. A method for public place security video surveillance management as claimed in claim 5, characterized in that: The image matching according to the calculation results is specifically as follows: Set the matching degree judgment threshold , If the Euclidean distance between the two video frames and the threshold value comparison satisfies the formula , indicating that the two video frames match; If the Euclidean distance between the two video frames and the threshold value comparison satisfies the formula , indicating that the two video frames do not match; Iterate over all video frame pairs , and the results that satisfy the matching degree form a set of matching images, then, in, Represents the formed matching image set of public place monitoring area, represents the calculated Euclidean distance, Indicates the matching degree judgment threshold, Represents a video frame pair.

7. A method for public place security video surveillance management as claimed in claim 6, characterized in that: The cross validation is specifically as follows: Build a scene layout model based on camera deployment information , Including, camera position , Camera View And scene partition information ; Based on the historical abnormal behavior database, set the abnormal behavior label set ; Before abnormal behavior detection, the constructed scene layout is cross-validated, specifically: in, Indicates Camera and The distance between the cameras, Indicates the distance threshold between the two cameras. Indicates scene partition information. It is used to limit the division of scene partitions to not exceed the scene partition range information. Indicates the viewing angle of the camera. Indicates the minimum number of cameras covered. Indicates the indicator function. When the value is 1, it indicates Covered by the camera's view, Indicates the total number of deployed cameras, Indicates the total number of scene partitions.

8. A method for public place security video surveillance management as claimed in claim 7, characterized in that: The use of anomaly detection algorithms to detect potential abnormal behaviors and security risks is specifically as follows: If the behavior feature vector With behavior tag collection The feature comparison results in satisfy the formula When , it means that the behavior feature extracted from the video data of the current monitoring area is consistent with the label in the constructed abnormal behavior label set, and the current behavior feature is abnormal behavior, where, represents the structural similarity between two features. Indicates the set structural similarity threshold; If the behavior feature vector With behavior tag collection The feature comparison results in satisfy the formula , it means that the behavior feature extracted from the video data of the current monitoring area does not match the label in the constructed abnormal behavior label set, and the current behavior feature is not an abnormal behavior.

9. A public place security video surveillance management system, applied to a public place security video surveillance management method according to claims 1 to 8, characterized in that: Including video data acquisition and processing module, image matching module and abnormal behavior recognition module, The video data acquisition processing module is used to acquire camera video data within the monitoring area of ​​the public place and filter out candidate video streams from the video data; The image matching module performs image matching on the video frame corresponding to the surveillance area video and the candidate video stream, and acquires the matching image of the surveillance area of ​​the public place based on the image matching result; The abnormal behavior identification module checks for potential abnormal behaviors and safety hazards based on the constructed public place monitoring area matching image set.

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