Crowd behavior identification

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Abstract
In this paper we present a novel method for crowd behavior identification. In our method, the motion flow field is obtained from the video by computing the dense optical flow. Then, a thermal diffusion process (TDP) is exploited to increase the coherence of the motion flow. Approximating the moving particles to individuals, their interaction forces are computed using a modified variant of the social force model (M-SFM) to highlight potential particles of interest. Besides capturing the effect of neighboring individuals on each other, the M-SFM also takes into account the crowd disorder, usually triggered by regions of high interactions. The experimental evaluation is conducted on a set of benchmark video sequences, commonly used for crowd motion analysis, and the obtained results are compared against a state of the art technique.

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