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	Background Subtraction via generalized fused lasso foreground modeling
	Xin, Bo ; Tian, Yuan ; Wang, Yizhou ; Gao, Wen
	2015
英文摘要	Background Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, due to challenges such as illumination change, dynamic background etc. the resulted foreground segmentation often consists of holes as well as background noise. In this regard, we consider generalized fused lasso regularization to quest for intact structured foregrounds. Together with certain assumptions about the background, such as the low-rank assumption or the sparse-composition assumption (depending on whether pure background frames are provided), we formulate BS as a matrix decomposition problem using regularization terms for both the foreground and background matrices. Moreover, under the proposed formulation, the two generally distinctive background assumptions can be solved in a unified manner. The optimization was carried out via applying the augmented Lagrange multiplier (ALM) method in such a way that a fast parametric-flow algorithm is used for updating the foreground matrix. Experimental results on several popular BS data sets demonstrate the advantage of the proposed model compared to state-of-the-arts. ? 2015 IEEE.; EI; 4676-4684; 07-12-June-2015
语种	英语
出处	IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
DOI标识	10.1109/CVPR.2015.7299099
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436566]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Xin, Bo,Tian, Yuan,Wang, Yizhou,et al. Background Subtraction via generalized fused lasso foreground modeling. 2015-01-01.

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