1. School of Computer and Electronic Information, Guangxi University, Nanning, Guangxi 530004, China;
2. Guangxi Key Laboratory of Multimedia Communications and Network Technology(Cultivating Base), Guangxi University, Nanning, Guangxi 530004, China;
3. Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing, Guangxi University, Nanning, Guangxi 530004, China
Abstract：The traditional robust principal component analysis (RPCA)model is able to solve the video foreground detection problem well.However,if the basic assumptions are violated,this model will have poor performance.This paper proposes a low rank and reweighted sparse decomposition model,where the foreground matrix is reweighted so as to enhance its sparsity.When the weighting matrix is established,the optical flow method is used to get the motion vectors in each frame in order that the real moving areas can be recognized.Afterwards,based on the newly proposed model,an enhanced decomposition model is also developed.Since the weighting matrix is applied to both the observation matrix and the background matrix,the enhanced model is able to prevent the foreground and the background from being wrongly separated.Experimental results show that the proposed algorithm can efficiently separate foreground and background components for video clips with or without noises.
[1] 杨威,付耀文,龙建乾,等.基于有限集统计学理论的目标跟踪技术研究综述[J].电子学报,2012,40(7):1440-1448. Yang W,Fu Y W,Long J Q,et al.The FISST-based target tracking techniques:a survey[J].Acta Electronica Sinica,2012,40(7):1440-1448.(in Chinese)
[2] Bouwmans T.Traditional and recent approaches in background modeling for foreground detection:an overview[J].Computer Science Review,2014,11:31-66.
[3] Zivkovic Z.Improved adaptive Gaussian mixture model for background subtraction[A].Proceedings of the 17th International Conference on Pattern Recognition[C].Cambridge,UK:IEEE,2004.28-31.
[4] Elgammal A,Duraiswami R,Harwood D,et al.Background and foreground modeling using nonparametric kernel density estimation for visual surveillance[J].Proceedings of the IEEE,2002,90(7):1151-1163.
[5] Zhou Y,Xu W,Tao H,et al.Background segmentation using spatial-temporal multi-resolution MRF[A].Proceedings of the 7th IEEE Workshop on Applications of Computer Vision/Motion and Video Computing[C].Breckenridge,USA:IEEE,2005.8-13.
[6] Reddy V,Sanderson C,Lovell B C.Improved foreground detection via block-based classifier cascade with probabilistic decision integration[J].IEEE Transactions on Circuits and Systems for Video Technology,2013,23(1):83-93.
[7] Candès E J,Li X,Ma Y,et al.Robust principal component analysis?[J].Journal of the ACM,2011,58(3):1-37.
[8] 彭义刚,索津莉,戴琼海,等.从压缩感知到低秩矩阵恢复:理论与应用[J].自动化学报,2013,39(7):981-994. Peng Y G,Suo J L,Dai Q H,et al.From compressed sensing to low-rank matrix recovery:theory and applications[J].Acta Automatica Sinica,2013,39(7):981-994.(in Chinese)
[9] Peng Y G,Suo J L,Dai Q H,et al.Reweighted low-rank matrix recovery and its application in image restoration[J].IEEE Transactions on Cybernetics,2014,44(12):2418-2430.
[10] Liu X,Zhao G Y,Yao J W,et al.Background subtraction based on low-rank and structured sparse decomposition[J].IEEE Transactions on Image Processing,2015,24(8):2502-2514.
[11] Ye X C,Yang J Y,Sun X,et al.Foreground-background separation from video clips via motion-assisted matrix restoration[J].IEEE Transactions on Circuits and Systems for Video Technology,2015,25(11):1721-1734.
[12] Wen J J,Xu J,Zhan Y W,et al.Joint video frame set division and low-rank decomposition for background subtraction[J].IEEE Transactions on Circuits and Systems for Video Technology,2014,24(12):2034-2048.
[13] Zhao Q,Meng D,Xu Z,et al.Robust principal component analysis with complex noise[A].Proceedings of the 31th International Conference on Machine Learning[C].Beijing,China:IEEE,2014.55-63.
[14] Chang K,Ding P K,Li B.Compressive sensing reconstruction of correlated images using joint regularization[J].IEEE Signal Processing Letters,2016,23(4):449-453.
[15] Lin Z,Chen M,Wu L,et al.The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low Rank Matrices[R].Urbana-Champaign:University of Illinois at Urbana-Champaign,2009.
[16] Goldstein T,Osher S.The split Bregman method for l1 regularized problems[J].SIAM Journal on Image Sciences,2009,2(2):322-343.
[17] Wang Y,Jodoin P M,Porikli F,et al.CDnet 2014:an expanded change detection benchmark dataset[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops[C].Columbus,OH,USA:IEEE,2014.387-394.
[18] Liu C.Beyond Pixels:Exploring New Representations and Applications for Motion Analysis[D].Cambridge:Department of Electrical Engineering and Computer Science,Massachusetts Institute of Technology,2009.
[19] Brox T,Bruhn A,Papenberg N,et al.High accuracy optical flow estimation based on a theory for warping[A].Proceedings of the European Conference on Computer Vision[C].Prague,Czech Republic:Springer,2004.25-36.
2017, Vol. 45 
