人群行为识别是计算机视觉领域的重要研究课题，针对小规模松散人群兼具微观层面与宏观层面行为特征这一特点，提出了一种基于因果网络分析的小规模人群行为识别方法。先将各行人目标看成网络的节点，利用协方差跟踪获得目标的运动轨迹，并利用Granger因果关系检验来评估目标之间的相互作用，并用此因果关系来构建成对因果网络和成组因果网络，计算复杂网络的平均路径长度，介数，聚类系数等参数特性，以表达和识别聚集、聊天、分离、徘徊、相遇及同行等6种人群行为，实验结果表明，提出的算法能够有效的表达和识别人群行为。

Crowd behavior recognition is an important research topic in computer vision field. Amid at the properties that the behavior of small scale crowd have the features both microcosmic and macroscopic, a small scale crowd recognition method based on causality network analysis is proposed. The trajectories of each pedestrians are calculated by covariance tracking to gain the nodes of crowd network. The Granger causality test is used to estimate the relationship between two pedestrians. Based on these causations, two types of complex network are generated which are pair-complex network and group-complex network. Some features of network such as the average path length, betweenness and clustering coefficient are extracted to recognize the six classifications crowd behavior (gather, chat, split, linger, meet and together). Experimental results show that the proposed method can express and recognize crowd behavior effectively.