Abstract
Abnormal detection in crowd is a challenging vision task due to the scarcity of real-world training examples and the lack of a clear definition of abnormality. To tackle these challenges, we propose a novel measure to capture the commotion of a crowd motion for the task of abnormality detection in crowd. The unsupervised nature of the proposed measure allows to detect abnormality adaptively (i.e. context dependent) with no training cost. The extensive experiments on three different levels (e.g. pixel, frame and video) show the superiority of the proposed approach compared to the state of the arts.
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