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We propose a non-parametric hierarchical event model to perform online anomaly detection in videos. A dynamic exemplar set is first used to represent observed event samples which updates itself every time when a new sample comes in. Upon this set, clusters are extracted to summarize the exemplars, offering a compact yet informative data structure for past event samples. Abnormal events are detected...
Detecting abnormal behaviors in crowd scenes is quite important for public security and has been paid more and more attentions. Most previous methods use offline trained model to perform detection which can't handle the constantly changing crowd environment. In this paper, we propose a novel unsupervised algorithm to detect abnormal behavior patterns in crowd scenes with online learning. The crowd...
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