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Public place abnormal behavior automatic identification method and device, and camera equipment

A public place, automatic identification technology, applied in the direction of character and pattern recognition, computer parts, instruments, etc., can solve the problems of poor monitoring effect and low recognition accuracy

Pending Publication Date: 2020-12-15
HUBEI UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] The present invention provides a method, device and camera equipment for automatic recognition of abnormal behaviors in public places, which are used to solve the technical problems in the prior art that the recognition accuracy of abnormal behaviors is not high and the monitoring effect is not good

Method used

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  • Public place abnormal behavior automatic identification method and device, and camera equipment
  • Public place abnormal behavior automatic identification method and device, and camera equipment
  • Public place abnormal behavior automatic identification method and device, and camera equipment

Examples

Experimental program
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Effect test

Embodiment 1

[0080] This embodiment provides an automatic identification method for abnormal behavior in public places, please refer to figure 1 , the method includes:

[0081] S1: Perform multi-target dynamic detection on the collected images, and the detection algorithm uses the YOLO series algorithm to identify human targets;

[0082] S2: Using fast corner detection and LK optical flow method to estimate the tracking speed of the identified human target, and identify the target whose moving speed exceeds the speed threshold, and regard it as an abnormal target;

[0083] S3: Determine whether the density of human targets in the preset pixel area is greater than the density threshold, and if it is greater, it is determined to be an abnormal behavior;

[0084] S4: Calculate the confidence and affinity vectors of human key points for the identified human targets, then perform key point cluster analysis, distinguish and connect key points to complete the assembly of human skeletons, realize...

Embodiment 2

[0115] Based on the same inventive concept, this embodiment provides an automatic identification device for abnormal behavior in public places, please refer to figure 2 , the device consists of:

[0116] The human target detection unit 201 is used to perform multi-target dynamic detection on the collected images, and the detection algorithm uses YOLO series algorithms to identify human targets;

[0117] An abnormal speed target recognition unit 202, configured to use fast corner detection and LK optical flow method to estimate the tracking speed of the identified human target, and identify the target whose moving speed exceeds the speed threshold, and use it as an abnormal target;

[0118] The dense target identification unit 203 is used to determine whether the density of human targets in the preset pixel area is greater than the density threshold, and if it is greater, it is determined to be an abnormal behavior;

[0119] Abnormal posture target recognition unit 204 is use...

Embodiment 3

[0123] Based on the same inventive concept, this embodiment provides a camera device, including the device for automatic recognition of abnormal behavior in public places described in Embodiment 2, a high-precision pan / tilt module, a long-distance optical imaging module, and a coordinate transformation module,

[0124] The high-precision gimbal module is used to realize the application requirements of various scenarios by carrying lenses of different specifications, including the horizontal high-precision attitude system and the vertical high-precision attitude system;

[0125] The long-distance optical imaging module is a white light optical sensing CCD module with a 100x optical zoom function for collecting video data within a field of view of 1500 meters;

[0126] The coordinate transformation module is used to calculate the coordinates in the station center coordinate system according to the abnormal target pixel coordinates and posture data identified by the abnormal behav...

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Abstract

The invention discloses a public place abnormal behavior automatic identification method and device, and camera equipment, and the method comprises the steps: firstly carrying out the multi-target dynamic detection of a collected image, using a YOLO series algorithm as a detection algorithm for identifying a human target; secondly, performing tracking speed estimation on the identified human target by fast corner detection and an LK optical flow method, identifying a target of which the moving speed exceeds a speed threshold value, and taking the target as an abnormal target; determining whether the human target density in the preset pixel area is greater than a density threshold value or not, and if so, determining that the behavior is abnormal; performing human body key point confidencecoefficient and affinity vector calculation on the identified human target, performing key point clustering analysis, distinguishing connection keys to complete human body skeleton building and assembling, realizing human body posture estimation, and determining whether a posture is abnormal or not according to a human body posture estimation result.

Description

technical field [0001] The invention relates to the technical field of video surveillance, in particular to a method and device for automatically identifying abnormal behaviors in public places, and camera equipment. Background technique [0002] With the rapid development of today's society and economy, and the acceleration of urbanization, there are often peak flow of people in various public places in cities and towns, such as shopping malls, stations, stadiums, banks, schools, etc. The crowded flow of people has brought great hidden dangers to public safety. In order to ensure For public safety, maintaining public order, responding to emergencies, and effectively combating crimes, a large number of video surveillance systems have been put into use, but the current video surveillance has the following problems: [0003] 1. The back-end server mainly relies on manual interpretation, which is inefficient. In recent years, some video analysis methods based on artificial inte...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/20G06V20/53G06V2201/07G06F18/23G06F18/22
Inventor 晋建志徐斌何伍斌范君涛冯毓伟李永逵陈博
Owner HUBEI UNIV OF SCI & TECH
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