Identification of Trajectory Anomalies on Video Surveillance Systems

Recently, CCTV surveillance applications have remarkably developed for public welfare. However, the investigation of different techniques for online implementation is always significantly restricted. Numerous implementations propose for detecting irregularities of moving objects in the videotape. Performance of fuzzy in trajectory's anomaly is one of the most robust detection procedures. In this paper, the authors propose a fuzzy implemented trajectory anomalies detection technique with the help of some parameters such as velocity, path deviation, and size of the moving objects. The critical aspect of the framework is a compact set of highly descriptive features extracted from a novel cell structure that helps us define support regions in a coarse-to-fine fashion. This paper also illustrates a small outline of different detection techniques. The authors also exhibit the outcome of experiments on the Queen Mary University of London junction dataset (QMUL).