基于Girvan-Newman分裂的人群异常事件检测算法

Detection Algorithm of Crowd Abnormal Event Based on Girvan-Newman Splitting

针对传统基于群体运动状态分析的异常事件检测方法对场景语义信息描述不足的问题,引入了复杂网络中运用社区发现的Girvan-Newman(GN)分裂算法。将具有相似运动特征且位置相近的行人划分为多个群组,利用群组运动强度和群组数量的变化,描述群组在正常和异常场景中的差异,检测异常事件的发生。通过实验验证,该算法能够在丰富场景语义信息的同时实现对异常事件的准确检测。

In terms of the problem that the traditional detection method of crowd abnormal events based on group motion state analysis does not describe the semantic information of scene adequately,the Girvan-Newman(GN)splitting up algorithm found by the community in the complex network is introduced.The pedestrians with similar motion characteristics and similar positions are divided into multiple groups,and the differences among the groups in normal and abnormal scenes are described and the occurrence of abnormal events is detected with the changes in group motion intensity and group number.Through experimental verification,the proposed algorithm can accurately detect abnormal events while enriching the semantic information of the scene.