Crowd density detection method based on crowd gathering mode and multi-column convolutional neural network

Abstract

Crowds and stampedes often occur in crowd gathering places, resulting in a large number of casualties and causing great negative social impacts. Traditional research on the dynamic assessment of crowd gathering safety mainly relies on real-time video monitoring, but lacks reliable methods for processing a large amount of video data from different sources, different perspectives and different granularities. Based on Edward Hall's personal space theory, this article considers crowd psychology and other factors, and establishes static basic model of crowd gathering patterns. In order to fuse real-time multi-granularity surveillance videos with different perspectives, a multi-column convolutional neural network (M-CNN) was used to extract the local density characteristics of the crowd in a low-altitude perspective, thereby establishing a holographic model of the temporal and spatial evolution of the crowd situation, and a new crowd gathering safety assessment method. This method was actually applied to the safety assessment of crowd gathering in Suzhou landmark-Urban Living Fountain Square, and achieved good results, providing theoretical support for the safety management of crowd gathering places.