Stream-based Active Unusual Event Detection

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Stream-based Active Unusual Event Detection
We present a new active learning approach to incorporate human feedback for on-line unusual event detection. In contrast to most existing unsupervised methods that perform passive mining for unusual events, our approach automatically requests supervision for critical points to resolve ambiguities of interest, leading to more robust and accurate detection on subtle unusual events. The active learning strategy is formulated as a stream-based solution, i.e. it makes decision on-the-fly on whether to query for labels. It adaptively combines multiple active learning criteria to achieve (i) quick discovery of unknown event classes and (ii) refinement of classification boundary. Experimental results on busy public space videos show that with minimal human supervision, our approach outperforms existing supervised and unsupervised learning strategies in identifying unusual events. In addition, better performance is achieved by using adaptive multi-criteria approach compared to exis...
Chen Change Loy, Tao Xiang, Shaogang Gong
Added 06 Apr 2011
Updated 07 Jan 2012
Type Conference
Year 2010
Where ACCV
Authors Chen Change Loy, Tao Xiang, Shaogang Gong
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