Method for detecting group crowd abnormal behaviors in video monitoring

Abstract

The invention provides a method for detecting group crowd abnormal behaviors in video monitoring. The method comprises the steps of: video target detection: obtaining video objects through edge information difference detection in successive frames, and obtaining video objects with movement change through frame difference of a foreground frame and a background frame, obtaining a relatively accurate movement target by combining two video object detection results; video target tracking: tracking targets to obtain corresponding movement tracks through a video particle-based long-period movement estimation method; group crowd detection: carrying out spectral clustering analysis on the distance between the tracks and advancing speed information through movement characteristics of the group crowd in video; and identification of group crowd abnormal behaviors: establishing a model for crowd tracks by using an MGHMM (Mixed Gaussian Hidden Markov Model), and identifying blockage and fall through sudden change of a normal track. The invention integrates technologies of crowd target detection, group target track, mode identification and machine learning.

Description

Technical field [0001] The invention relates to a method for detecting abnormal behavior of crowds, in particular to a method for detecting abnormal behaviors of group crowds based on video monitoring analysis, and belongs to the field of video monitoring applications and technology integration. Background technique [0002] Although the current intelligent surveillance system for crowd recognition has received some attention in recent years, most studies have focused on determining the number of people in a small spatial area (which has been calculated in the crowd calculation and tracking paradigm). There are relatively few researches on crowd behavior analysis, and there are few related researches that can solve population detection and tracking at the level of medium-density or small group. [0003] The intelligent monitoring system for the crowd consists of four main parts: crowd detection, crowd tracking, crowd classification and crowd abnormal behavior recogniti...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems existing in the prior art, such as the dynamic movement of crowds, occlusion or local gathering problems, and environmental interference affecting the crowd target recognition system among sports crowds, the present invention provides a group based on video surveillance The method for detecting crowd abnormal behavior includes the following steps:

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