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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...
In this paper, we present a novel approach for video anomaly detection in crowded scenes. The proposed approach detects anomalies based on the contextual information analysis within spatio-temporal video volume. Around each pixel, spatio-temporal volumes are built and clustered to construct the activity pattern codebook. Then, the composition information of the volumes within a large spatiotemporal...
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