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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
Conference NameCVPR Workshop on Visual Surveillance
Source PublicationIEEE Conference on Computer Vision & Pattern Recognition 2008
Pages1-8
Conference Date2008
Conference PlaceMarseille , France
AbstractIn 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.
KeywordKde-gmm Hybrid Model
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12708
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
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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