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