Small scale crowd behavior classification by Euclidean distance variation-weighted network

Abstract

Crowd behavior analysis is a key research topic in the field of computer vision. The traditional method of crowd behavior analysis can be divided into two categories. One is the microscopic-based method, which is mainly through extracting the gestures and trajectories of every individual to recognize the behavior of a crowd. The other is the macroscopic-based method, in which the statistical characteristics of a crowd are used to represent the crowd behavior. By exploring the connection between the microscopic and macroscopic properties of a crowd, a method which use Euclidean distance variation-weighted network to recognize the crowd behavior is proposed in this paper. Firstly, the trajectories and location information of the individuals in crowd are captured by tracking every pedestrian. Furthermore, by calculating the Euclidean distance variation between two individuals the interaction between individuals is gained. Then, the Euclidean distance variation-weighted networks of five typical crowd behavior including gather, meet, together, separation and dispersion are constructed. The nodes represent individuals in the crowd and the weight of each edge represents the extent of interaction between individuals. Finally, the characteristic parameters of crowd networks including the path length and network weights are extracted and the crowd behavior is classified by using k-nearest neighbor method. Experimental results show the proposed method can effectively express and recognize small-scale crowd behavior. The lowest classification accuracy of the proposed method can reach 86.8 % in the five kinds of crowd behavior.