A Probabilistic Framework Based on KDE-GMM hybrid model (KGHM) for Moving Object Segmentation in Dynamic Scenes

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	A Probabilistic Framework Based on KDE-GMM hybrid model (KGHM) for Moving Object Segmentation in Dynamic Scenes
	Zhou Liu; Wei Chen; Kaiqi Huang; Tieniu Tan
	2008
会议日期	2008
会议地点	Marseille , France
关键词	Kde-gmm Hybrid Model
页码	1-8
英文摘要	In real scenes, dynamic background and moving cast shadow always make accurate moving object detection difficult. In this paper, a probabilistic framework for moving object segmentation in dynamic scenes is proposed. Under this framework, we deal with foreground detection and shadow removal simultaneously by constructing probability density functions (PDFs) of moving objects and non-moving objects. Here, these PDFs are constructed based on KDEGMMhybrid model (KGHM) which has advantages of KDE and GMM. This KGHM models the spatial dependencies of neighboring pixel colors to deal with highly dynamic scenes. Moreover, in this framework, tracking information is used to refine the PDF of moving objects. Experimental results demonstrate the effectiveness of our method.
会议录	IEEE Conference on Computer Vision & Pattern Recognition 2008
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/12708]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Kaiqi Huang
作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhou Liu,Wei Chen,Kaiqi Huang,et al. A Probabilistic Framework Based on KDE-GMM hybrid model (KGHM) for Moving Object Segmentation in Dynamic Scenes[C]. 见:. Marseille , France. 2008.