中文摘要：

提出一种图分析方法用于动态人群场景异常状态检测.使用自适应Mean shift算法对场景速度场进行非参数概率密度估计聚类,聚类结果构成以聚类中心为顶点、各聚类中心之间距离为边权重的无向图.通过分析图顶点的空间分布及边权重矩阵动态系统的预测值与观测值之间的离散程度,对动态场景中的异常事件进行检测和定位.使用多个典型动态场景视频数据库进行对比实验,结果表明图分析方法适应性强、可有效监控动态人群场景中的异常状态.

英文摘要：

An abnormity detection method for a dynamic crowd scene is proposed based on graph analysis.After the non-parametric clustering in velocity space via an adaptive mean shift algorithm,we get the clustering results containing some cluster centers and Euclidean distances between them,and they can form a graph whose vertexes are the cluster centers and edge weights are the distances.Through analyzing the vertexes distribution in feature space and the state transform of a dynamic system made by the sequence of the edge weight matrix,we can detect and locate the abnormal events in the scenario.To testify the method s effectiveness,we conducted experiments on several well-known datasets and obtained good performance in both abnormal events detection and location.The results show that the graph analysis method has strong adaptability and can efficiently detect the abnormal states in dynamic crowd scene.