Jiang, Fan and Yuanc, Junsong and Tsaftaris, Sotirios A. and Katsaggelos, Aggelos K. Anomalous video event detection using spatiotemporal context. Computer vision and image understanding, 115 (3). 323 - 333. ISSN 1077-3142 (2011)Full text not available from this repository.
Compared to other anomalous video event detection approaches that analyze object trajectories only, we propose a context-aware method to detect anomalies. By tracking all moving objects in the video, three different levels of spatiotemporal contexts are considered, i.e., point anomaly of a video object, sequential anomaly of an object trajectory, and co-occurrence anomaly of multiple video objects. A hierarchical data mining approach is proposed. At each level, frequency-based analysis is performed to automatically discover regular rules of normal events. Events deviating from these rules are identified as anomalies. The proposed method is computationally efficient and can infer complex rules. Experiments on real traffic video validate that the detected video anomalies are hazardous or illegal according to traffic regulations.
|Additional Information:||Special issue on Feature-Oriented Image and Video Computing for Extracting Contexts and Semantics|
|Uncontrolled Keywords:||Video surveillance; Anomaly detection; Data mining; Clustering; Context|
|Subjects:||T Technology > TK Electrical engineering. Electronics Nuclear engineering|
|Research Area:||Computer Science and Applications|
|Depositing User:||Users 35 not found.|
|Date Deposited:||11 Aug 2011 12:06|
|Last Modified:||05 Mar 2013 15:14|
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