Abnormal Event Detection in Traffic Video Surveillance Based on Local Features
In this article, we proposed an abnormal event detection method based on local features for traffic video surveillance. Firstly, foreground assumed to be moving is detected and affined with morphological operations. Then each foreground regions area, shape factors (ellipse eccentricity, width-height radio of outside rectangular, and etc.), and pixel moving velocity vector are extracted. Based on those features, regions are classified into different groups as pedestrian, vehicle or noise region, and their behavior is classified using trained local features distribution map (location distribution and velocity distribution). Finally, a simple classifier is used to determine objects states of normal or abnormal. With the rapid development of ITS (Intelligent Traffic Surveillance), our low complexity and low level abnormality detection method is well fit in early alarm of distributed surveillance system. We have some experiment to show the benefits of proposed method.
Optical flow local features extraction video surveillance abnormal event detection
Lili Cui Kehuang Li Jiapin Chen Zhenbo Li
Shanghai Jiao Tong University Shanghai, China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
372-376
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）