Abnormal Behavior Detection In Crowd Based On Video

With the rapid development of society and economy,the accelerating urbanization process,and the frequent occurrence of safety accidents in public places,the traditional video surveillance technology has been unable to meet the needs of social development and progress.Therefore,it is urgent to develop a kind of intelligent video surveillance technology to meet the needs of social development.Crowd behavior anomaly detection has become one of the hot issues in the field of video surveillance.This technique mainly includes the automatic motion object extraction,feature extraction,tracking,recognition and behavior analysis of the video image sequence.The research on anomaly detection algorithm for crowd behavior is a challenging problem,including many key problems in computer vision,image processing and artificial intelligence.Feature extraction and detection for the abnormal state of crowd behavior such as riot and escape in public places are studied in this paper.An algorithm based on the modified social force model to detect the abnormal behavior of the crowd and the anomaly detection algorithm based on the optical flow co-occurrence matrix are proposed from different angles.The research includes the extraction of motion information and the detection of feature points,modeling and calculating the social forces of pedestrians,Optical flow co-occurrence matrix establishment and feature extraction,feature classification and so on.The main research is as follows:1)In view of the shortcomings of the classic social force model,it was modified by introducing the different effects of pedestrians relative velocity and position.The shortcomings of pedestrian social force calculation method using grid particle sampling method is analyzed and an improved algorithm based on the modified social force model is proposed in this paper.First,the Lucas-Kanade optical flow algorithm is used to extract the video pedestrian movement information.And then Harris corner detection algorithm is used to detect pedestrian corner.The social forces between the moving pedestrian corners are calculated according to the modified social forcemodel.Finally the characteristics of crowd movement behavior are extracted based on the social force.2)Based on the study and research on the algorithm of population behavior anomaly detection that based on dynamic texture feature,it is essentially a feature descriptor of image gray scale texture which does not accurately reflect the movement information of the crowd.According to the characteristics of sports information space division in different population behavior state,a new method to extract the distribution of motion information of the population by establishing the optical flow co-occurrence matrix is proposed in this paper.3)In this paper,the algorithm of crowd behavior anomaly detection based on modified social force model and the algorithm of crowd behavior anomaly detection based on optical flow co-occurrence matrix are described respectively.The performance of the algorithms is verified by different video data sets.In the last part of this paper,the experimental results of the methods given by this paper are compared with those of the algorithms in the literature,which proves the superiority of the algorithms given by this paper in the detection of population behavior anomaly.