Stream-Based Active Unusual Event Detection
Stream-Based Active Unusual Event Detection
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DOI：
10.1007/978-3-642-19315-6_13
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发表时间：
2010-11
期刊：
影响因子：
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通讯作者：
Chen Change Loy;T. Xiang;S. Gong
中科院分区：
文献类型：
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作者：
Chen Change Loy;T. Xiang;S. Gong
我们提出了一种新的主动学习方法，将人类反馈纳入在线异常事件检测。与大多数现有的对异常事件进行被动挖掘的无监督方法相比，我们的方法自动请求对关键点进行监督以解决感兴趣的歧义，从而对微妙的异常事件进行更稳健和准确的检测。主动学习策略被制定为基于流的解决方案，即它即时做出是否查询标签的决定。它自适应地结合多个主动学习标准，以实现（i）快速发现未知事件类别和（ii）细化分类边界。在繁忙的公共空间视频上的实验结果表明，在最少的人类监督下，我们的方法在识别异常事件方面优于现有的监督和无监督学习策略。此外，与现有的单标准和多标准主动学习策略相比，使用自适应多标准方法可以获得更好的性能。
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 existing single criterion and multi-criteria active learning strategies.