采用低秩与加权稀疏分解的视频前景检测算法

doi:10.3969/j.issn.0372-2112.2017.09.031

电子学报 ›› 2017, Vol. 45 ›› Issue (9): 2272-2280.DOI: 10.3969/j.issn.0372-2112.2017.09.031

采用低秩与加权稀疏分解的视频前景检测算法

常侃^1,2,3, 张智勇¹, 陈诚¹, 覃团发^1,2,3

1. 广西大学计算机与电子信息学院, 广西南宁 530004;
2. 广西大学广西多媒体通信与网络技术重点实验室培育基地, 广西南宁 530004;
3. 广西大学广西高校多媒体通信与信息处理重点实验室, 广西南宁 530004

收稿日期:2016-08-16 修回日期:2017-01-19 出版日期:2017-09-25
- 通讯作者:
- 常侃
- 作者简介:
- 张智勇,男,1991年9月出生于广西扶绥.现为广西大学计算机与电子信息学院计算机技术专业硕士研究生,主要研究方向为稀疏表示、低秩矩阵及应用等.E-mail:zhangzhiyong1160@163.com;陈诚,男,1993年10月出生于安徽舒城.现为广西大学计算机与电子信息学院信息处理与通信网络系统学术硕士研究生,主要研究方向为图像处理、稀疏表示等.E-mail:vigil1993@163.com;覃团发,男,1966年7月出生于广西宾阳.1997年于南京大学获博士学位,现为广西大学计算机与电子信息学院副院长、教授、博士生导师.主要研究方向为多媒体通信、无线传感器网络等.E-mail:tfqin@gxu.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61401108）; 广西自然科学基金 (No.2016GXNSFAA380154）

Video Foreground Detection by Low-Rank and Reweighted Sparse Decomposition

CHANG Kan^1,2,3, ZHANG Zhi-yong¹, CHEN Cheng¹, QIN Tuan-fa^1,2,3

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

Received:2016-08-16 Revised:2017-01-19 Online:2017-09-25 Published:2017-09-25
- Supported by:
- National Natural Science Foundation of China (No.61401108); Natural Science Foundation of Guangxi Zhuang Autonomous Region, China (No.2016GXNSFAA380154)

摘要/Abstract

摘要： 传统的鲁棒主成分分析模型能较好地解决视频前景检测问题.但是，若该模型的假设条件不能满足，算法性能会变差.针对此问题，本文提出了一种低秩与加权稀疏分解模型，通过对前景矩阵加权以增强其稀疏性.在建立加权矩阵的过程中，采用光流法获取每帧的运动矢量，以区分真实运动区域.其次，进一步提出一种增强模型，通过将加权矩阵作用于观测矩阵及背景矩阵，防止前景与背景的错误分离.实验结果表明，在无噪和有噪的情况下，提出的算法均能有效地分离监控视频中的前景和背景.

关键词: 前景检测, 运动目标检测, 鲁棒主成分分析, 低秩表示, 光流法

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.

Key words: foreground detection, moving object detection, robust principal component analysis (RPCA), low-rank representation, optical flow method

中图分类号:

TN919.8

常侃, 张智勇, 陈诚, 等. 采用低秩与加权稀疏分解的视频前景检测算法[J]. 电子学报, 2017, 45(9): 2272-2280.

CHANG Kan, ZHANG Zhi-yong, CHEN Cheng, et al. Video Foreground Detection by Low-Rank and Reweighted Sparse Decomposition[J]. Acta Electronica Sinica, 2017, 45(9): 2272-2280.

参考文献

[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.

采用低秩与加权稀疏分解的视频前景检测算法

Video Foreground Detection by Low-Rank and Reweighted Sparse Decomposition

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