Simulation of Abnormal Behavior Monitoring in Surveillance Video of Sudden Public Security Incidents
The abnormal targets in public safety event monitoring videos are usually small and medium-sized in-dividuals,and their monitoring process is also susceptible to interference from the surrounding public environment.In this paper,a method of monitoring abnormal behaviors in surveillance videos of sudden public security incidents was presented.First of all,we built an enhanced Cucker-Smale group motion model in line with the characteristics of hu-man abnormal behavior to describe the target motion function of human abnormal behavior.Based on above functions,we collected the abnormal behavior data related to sudden public security incidents in the surveillance videos.Moreo-ver,we used the optical flow block as a unit,and extracted the abnormal behavior data features.And then,these fea-tures were combined with K-means clustering algorithm.Finally,we comprehensively classified the abnormal behavior data features,thus achieving the monitoring on abnormal behavior in surveillance videos of sudden public security in-cidents.Simulation results show that the proposed method can identify the abnormal behavior in surveillance video ac-curately.The AUC value is up to 0.983.Therefore,the method has better application performance.
Sudden public security incidentsSurveillance videoAbnormal behaviorOptical flow blockClustering algorithm
NETL
NSTL
万方数据
维普
