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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
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.
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