Computer Science

Computer Science ›› 2018, Vol. 45 ›› Issue (9): 314-319.doi: 10.11896／j.issn.1002-137X.2018.09.053

• Graphics, Image & Pattern Recognition • Previous Articles    

Detection for Group Riot Activity Based on Change Analysis of Group Motion Pattern

HUANG Jin-guo1, LIU Tao1, ZHOU Xian-chun2, YAN Xi-jun3   

  1. School of Information Mechanical and Electrical Engineering,Jiangsu Open University,Nanjing 210017,China1
    School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210017,China2
    College of Computer and Information,Hohai University,Nanjing 210098,China3
  • Received:2017-08-30 Online:2018-09-20 Published:2018-10-10

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Abstract: Group riot activity is the main precaution point of intelligent video surveillance because of its large hazards for social public safety.In view of the problem of low efficiency and low detection accuracy of the existing group riot activity detection algorithms,an activity detection algorithm for group riot activity based on change analysis of group motion pattern was proposed.This method extracts the optical flow features of foreground pixels as the basis of the activity analysis,and uses K-means clustering and Bayesian criterion to realize the grouping division of different groups in the scene.On this basis,it analyzes the changes in motion patterns of all groups,builds the maximum change factors,and computes the variation of the maximum change factors to detect the group riot activities.The experimental results show that this method used for detecting group riot activities has low false-alarm rate and miss-alarm rate and less average detection time.

Key words: Activity detection, Group activity, K mean, Motion pattern, Optical flow

CLC Number: 

  • TP391
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