期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2014
卷号：2014
DOI：10.1155/2014/839045
出版社：Hindawi Publishing Corporation
摘要：Camera sensor networks have developed as a new technology for the wide-area video surveillance. In view of the limited power and computational capability of the camera nodes, the paper presents an abnormal behavior detection approach which is convenient and available for camera sensor networks. Trajectory analysis and anomaly modeling are carried out by single-node processing, whereas anomaly detection is performed by multinode voting. The main contributions of the proposed method are summarized as follows. First, target trajectories are reconstructed and represented as symbol sequences. Second, the sequences are taken into account using Markov model for building the transition probability matrix which can be used to automatically analyze abnormal behavior. Third, the final decision of anomaly detection is made through the majority voting of local results of individual camera nodes. Experimental results show that the proposed method can effectively estimate typical abnormal behaviors in real scenes.
