Nonlinear analysis of pedestrian flow Reynolds number in video scenes

Abstract

Abstract Research on crowd motion state plays an essential role for public safety and security. This paper aims to investigate the chaotic characteristics of pedestrian flow as a dynamical system. Firstly, pedestrian flow Reynolds number is proposed, which is a novel feature descriptor derived from Hydrodynamics, to describe crowd motion state. Secondly, the calculation method of pedestrian flow Reynolds number in video is put forward to characterize the motion state of the pedestrian flow. Thirdly, nonlinear time series analysis tools, including time delay embedding and largest Lyapunov exponent are applied to verify the chaos of pedestrian flow’s motion. Experiments are performed on different data sets and the result that all the largest Lyapunov exponent are positive could indeed demonstrate the complexity and chaos of crowd motion. Meanwhile, it turns out that pedestrian flow Reynolds number put forward in the paper can effectively characterize the motion state of pedestrian flow. Our work paves a new way for research on crowd turbulence. It could potentially be applied to pattern analysis of crowd abnormal behavior analysis, crowd motion understanding, which can be used to improve the efficiency of public security management.