Jiang, Fan and Yuanc, Junsong and Tsaftaris, Sotirios A. and Katsaggelos, Aggelos K. Video anomaly detection in spatiotemporal context. In: 17th IEEE international conference on image processing (ICIP). IEEE, 705 -708. ISBN 978-1-4244-7992-4 (2010)Full text not available from this repository.
Compared to other approaches that analyze object trajectories, we propose to detect anomalous video events at three levels considering spatiotemporal context of video objects, i.e., point anomaly, sequential anomaly, and co-occurrence anomaly. A hierarchical data mining approach is proposed to achieve this task. At each level, the frequency based analysis is performed to automatically discover regular rules of normal events. The events deviating from these rules are detected as anomalies. Experiments on real traffic video prove that the detected video anomalies are hazardous or illegal according to the traffic rule.
|Item Type:||Book Section|
|Uncontrolled Keywords:||anomalous video events; co-occurrence anomaly; frequency based analysis; hierarchical data mining approach; object trajectory; real traffic video anomaly detection; sequential anomaly; spatiotemporal context; video object; data mining; spatiotemporal phenomena; traffic; video surveillance|
|Subjects:||T Technology > TK Electrical engineering. Electronics Nuclear engineering|
|Research Area:||Computer Science and Applications|
|Depositing User:||Users 35 not found.|
|Date Deposited:||08 Sep 2011 13:25|
|Last Modified:||05 Mar 2013 15:33|
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