Abstract

Frequent public incidents in crowd gathering areas are causing social concerns. This paper first discusses different cases of crowd gathering based on Edward Hall’s personal space theory and construct a novel crowd gathering pattern model. Based on the model, our modified multi-column convolutional neural network is proposed for extracting the overcrowding. For evaluating its effectiveness, a heterogeneous multi-granularity real-time dynamic surveillance video containing different perspectives is integrated, and a new crowd gathering safety situation assessment method is applied. We finally report our real-world application in Suzhou landmark - Urban Fountain Square for crowd gathering safety situation assessment and show that the method can definitely improve the safety of crowd gathering areas.

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