NDLI: Background Subtraction via generalized fused lasso foreground modeling

Content Provider	IEEE Xplore Digital Library
Author	Bo Xin Yuan Tian Yizhou Wang Wen Gao
Copyright Year	2015
Description	Author affiliation: Key Lab. of Machine Perception, Peking Univ., Beijing, China (Bo Xin; Yuan Tian; Yizhou Wang; Wen Gao)
Abstract	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.
Starting Page	4676
Ending Page	4684
File Size	871076
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7299099
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Unified modeling language Optimization Matrix decomposition Sparse matrices Estimation Data models Adaptation models
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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