Video anomaly detection in spatiotemporal context. Jiang, F., Yuan, J., Tsaftaris, S. A., & Katsaggelos, A. K. In 2010 IEEE International Conference on Image Processing, pages 705–708, sep, 2010. IEEE.
Paper doi abstract bibtex 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. © 2010 IEEE.
@inproceedings{Fan2010,
abstract = {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. {\textcopyright} 2010 IEEE.},
author = {Jiang, Fan and Yuan, Junsong and Tsaftaris, Sotirios A. and Katsaggelos, Aggelos K.},
booktitle = {2010 IEEE International Conference on Image Processing},
doi = {10.1109/ICIP.2010.5650993},
isbn = {978-1-4244-7992-4},
issn = {15224880},
month = {sep},
pages = {705--708},
publisher = {IEEE},
title = {{Video anomaly detection in spatiotemporal context}},
url = {http://ieeexplore.ieee.org/document/5650993/},
year = {2010}
}
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