Crowd Anomaly Detection in Surveillance Video

Abstract

AbstractWith the advancement of technology, the analysis of crowd abnormal behavior has become a hot topic in the field of computer vision. The research not only includes the analysis of the abnormal behavior of a single pedestrian in a simple scene, but also includes the analysis of the overall abnormal behavior of the crowd in a complex scene. This paper research on the detection and alarm of abnormal crowd behavior in surveillance video. First, the moving target is detected by the background subtraction method. Secondly, the fall behavior in the video is detected through two-level SVM and human feature action recognition. At this stage, the human body features obtained in the target detection stage are analyzed, and the key point features of the human body are analyzed to determine whether the detection target has fallen behavior. Finally, a counting module is introduced to count the pedestrians on the basis of target detection in the current scene. We compare the changes in the number of people in the video frames at a set time interval in the same scene to determine whether a sudden crowd gathering occurred abnormal behavior.KeywordsCrowd anomaly detectionTwo-level SVMGMMCrowd counting