Graph-based Crowds Modeling for Video Surveillance
Modeling human crowds is an important issue for video surveillance and is a challenging task due to their nature of non-rigid shapes. In this paper, for real time constraint, Haar-like features are first employed to approximately locate the position of an isolated region that comprise an individual person or a set of occluded persons. Each isolated region is considered vertex and a human crowd is thus modeled by a graph. To regularly construct a graph, Delaunay triangulation is used to systematically connect vertices and therefore the problem of event detection of human crowds is formulated as measuring the topology variation of consecutive graphs in temporal order. To effectively model the topology variation, local characteristics such as triangle deformations and subgraph analysis, and global features such as moments are all computed and finally combined as an indicator to detect if any anomalies of human crowd( present in the scene. Experimental results obtained by using extensive dataset show that our system is effective in detecting anomalous events for uncontrolled environment of surveillance videos.
Duan-Yu Chen Po-Chung Huang
Department of Electrical Engineering, Yuan-Ze University
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-4
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）