Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis

Abstract

Recently, the demand for surveillance system is increasing in real time application to enhance the security system. These surveillance systems are mainly used in crowded places such as shopping malls, sports stadium etc. In order to support enhance the security system, crowd behavior analysis has been proven a significant technique which is used for crowd monitoring, visual surveillance etc. For crowd behavior analysis, motion analysis is a crucial task which can be achieved with the help of trajectories and tracking of objects. Various approaches have been proposed for crowd behavior analysis which has limitation for densely crowded scenarios, a new object entering the scene etc. In this work, we propose a new approach for abnormal crowd behavior detection. Proposed approach is a motion based spatio-temporal feature analysis technique which is capable of obtaining trajectories of each detected object.  We also present a technique to carry out the evaluation of individual object and group of objects by considering relational descriptors based on their environmental context. Finally, a classification is carried out for detection of abnormal or normal crowd behavior by following patch based process. In the results, we have reported that proposed model is able to achieve better performance when compared to existing techniques in terms of classification accuracy, true positive rate, and false positive rate.