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	Recursive Kernel Density Estimation for modeling the background and segmenting moving objects
	Zhu, Qingsong; Shao, LingLi, Q; Xie, Yaoqin
	2013
会议名称	2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
会议地点	Vancouver, BC, Canada
英文摘要	Identifying moving objects in a video sequence is a fundamental and critical task in video surveillance, traffic monitoring, and gesture recognition in human-machine interface. In this paper, we present a novel recursive Kernel Density Estimation based background modeling method. First, local maximum in the density functions is recursively approximated using a mean shift method. Second, components and parameters in the mixture Gaussian distributions can be selected adaptively via a proposed thresholding mechanism, and finally converge to a stable background distribution model. In the scene segmentation, foreground is firstly separated by simple background subtraction approach. And then a local texture correlation operator is introduced to fill the vacancies and remove the fractional false foreground regions so as to obtain a better video segmentation quality. Experiments conducted on synthetic and video data demonstrate the superior performance of the proposed algorithms.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/5013]
专题	深圳先进技术研究院_医工所
作者单位	2013
推荐引用方式 GB/T 7714	Zhu, Qingsong,Shao, LingLi, Q,Xie, Yaoqin. Recursive Kernel Density Estimation for modeling the background and segmenting moving objects[C]. 见:2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013. Vancouver, BC, Canada.

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