TY  - CHAP
ID  - eprints848
SN  - 978-1-4244-7992-4 
N2  - 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.
KW  - 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
AV  - none
EP  - 708
Y1  - 2010/09//
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5650993&isnumber=5648792
PB  - IEEE
T2  - 17th IEEE international conference on image processing (ICIP)
TI  - Video anomaly detection in spatiotemporal context
A1  - Jiang, Fan
A1  - Yuanc, Junsong 
A1  - Tsaftaris, Sotirios A.
A1  - Katsaggelos, Aggelos K.
SP  - 705 
ER  -