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In this work, we propose an unsupervised approach for detecting the anomalies in a crowd scene using social network model. Using a window-based approach, scene objects are first detected and tracked, and a spatio-temporal partitioning is constructed to produce a set of spatio-temporal cuboids that capture spatial and temporal features. A hierarchical social(More)
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