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In this paper, an abnormal event detection approach inspired by the saliency attention mechanism of human visual system is presented. Conventionally, statistics-based methods suffer from visual scale, complexity of normal events and insufficiency of training data, for the reason that a normal behavior model established from normal video data is used to detect unusual behaviors with an assumption that...
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...
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