Toward Abnormal Trajectory and Event Detection in Video Surveillance
In this paper, we present a unified approach for abnormal behavior detection and group behavior analysis in video scenes. Existing approaches for abnormal behavior detection do either use trajectory-based or pixel-based methods. Unlike these approaches, we propose an integrated pipeline that incorporates the output of object trajectory analysis and pixel-based analysis for abnormal behavior inference. This enables to detect abnormal behaviors related to speed and direction of object trajectories, as well as complex behaviors related to finer motion of each object. By applying our approach on three different data sets, we show that our approach is able to detect several types of abnormal group behaviors with less number of false alarms compared with existing approaches.
Citation : S. Coşar, G. Donatiello, V. Bogorny, C. Garate, L. O. Alvares and F. Brémond, (2017) Toward Abnormal Trajectory and Event Detection in Video Surveillance. IEEE Transactions on Circuits and Systems for Video Technology, 27 (3), pp. 683-695
Research Institute : Institute of Engineering Sciences (IES)
Peer Reviewed : Yes