Journal
NEUROCOMPUTING
Volume 155, Issue -, Pages 309-319Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2014.12.064
Keywords
Video anomaly detection; Contextual information; Bag-of-words; Sparse representation; Multi-scale analysis
Categories
Funding
- National Natural Science Foundation of China [61473277, 61403364]
- Shenzhen Nanshan District Key Enterprises and Innovative Institution Supporting Funds [KC2014ZDZJ0022A]
- Shenzhen Fundamental Research Program [JCYJ20140901003939022]
- Guangdong Innovative Research Team Program [201001D0104648280]
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In this paper, we propose an anomaly-detection approach applied for video surveillance in crowded scenes. This approach is an unsupervised statistical learning framework based on analysis of spatio-temporal video-volume configuration within video cubes. It learns global activity patterns and local salient behavior patterns via clustering and sparse coding, respectively. Upon the composition-pattern dictionary learned from normal behavior, a sparse reconstruction cost criterion is designed to detect anomalies that occur in video both globally and locally. In addition, a multiple scale analysis is employed for obtaining accurate anomaly localization, considering scale variations of abnormal events. This approach is verified on publically available anomaly-detection datasets and compared with other existing work. The experiment results demonstrate that it not only detects various anomalies more efficiently, but also locates anomalous regions more accurately. (C) 2015 Elsevier B.V. All rights reserved.
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