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

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	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
会议名称	CVPR Workshop on Visual Surveillance
会议录名称	IEEE Conference on Computer Vision & Pattern Recognition 2008
页码	1-8
会议日期	2008
会议地点	Marseille , France
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.
关键词	Kde-gmm Hybrid Model
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12708
专题	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
第一作者单位	中国科学院自动化研究所
通讯作者单位	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],2008:1-8.