Abstract: The target of analyzing crowd behavior is to better analyze and manage the state and tendency of crowd movement.This paper proposed a novel deep learning based crowd behavior recognition method by using two types of crowd behavior features.Firstly,the crowd is regarded as the main object,a foreground extraction method is used to extract the static information of crowd,and the dynamic information of crowd is obtained by the change of the crowd movement.Then two different crowd behavior characteristics are learned by using convolution neural network (CNN) model,so as to analyze crowd behaviors in the end.Additionally,the extraction location and interval of crowd data are crucial factors in the crowd behavior recognition.Experimental results show that two crowd characteristics can better describe crowd states on the spatial dimension and crowd changes on the temporal dimension.The rational data location and data interval can effectively improve the expression ability of crowd information.At last,this method was compared with other crowd behavior recognition algorithms.The quantitative and qualitative experimental results demonstrate the validity of the proposed method.Besides,better confusion matrix and higher precision can be obtained by this method.

Key words: CNN, Crowd behavior recognition, Data extraction, Dynamic characteristic, Static characteristic