View-invariant Action Recognition in Surveillance Videos

	View-invariant Action Recognition in Surveillance Videos
	Fang Zhang; Yunhong Wang; Zhaoxiang Zhang
	2011-11-28
会议日期	28th November 2011
会议地点	Beijing, China
关键词	Videos Gaussian Distribution Surveillance Shape Humans Hidden Markov Models Databases
英文摘要	Recently, human action recognition has been a popular and important topic in computer vision. However, except some conventional problems such as noise, low resolution etc., view-invariant recognition is one of the most challenging problems. In this paper, we focus on solve multi-view action recognition from surveillance video. To detect moving objects from complicated backgrounds, this paper employs improved Gaussian mixed model, which uses K-means clustering to initialize the model and it gets better motion detection results for surveillance videos. We demonstrate the silhouette representation “Envelope Shape” can solve the viewpoint problem in surveillance videos. The experiment results demonstrate that our human action recognition system is fast and efficient on CASIA activity analysis database.
会议录	ACPR 2011
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/13277]
专题	自动化研究所_类脑智能研究中心
通讯作者	Zhaoxiang Zhang
推荐引用方式 GB/T 7714	Fang Zhang,Yunhong Wang,Zhaoxiang Zhang. View-invariant Action Recognition in Surveillance Videos[C]. 见:. Beijing, China. 28th November 2011.