Background subtraction based on tensor nuclear norm and 3D total variation
CHEN Lixia1,2, BAN Ying1,2, WANG Xuewen3
1. School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin Guangxi 541004, China; 2. Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation(Guilin University of Electronic Technology), Guilin Guangxi 541004, China; 3. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
Abstract：Concerning the fact that common background subtraction methods ignore the spatio-temporal continuity of foreground and the disturbance of dynamic background to foreground extraction, an improved background subtraction model was proposed based on Tensor Robust Principal Component Analysis (TRPCA). The improved tensor nuclear norm was used to constrain the background, which enhanced the low rank of background and retained the spatial information of videos. Then the regularization constraint was performed to the foreground by 3D Total Variation (3D-TV), so as to consider the spatio-temporal continuity of object and effectively suppress the interference of dynamic background and target movement on the foreground extraction. Experimental results show that the proposed model can effectively separate the foreground and background of videos. Compared with High-order Robust Principal Component Analysis (HoRPCA), Tensor Robust Principal Component Analysis with Tensor Nuclear Norm (TRPCA-TNN) and Kronecker-Basis-Representation based Robust Principal Component Analysis (KBR-RPCA), the proposed algorithm has the F-measure values all optimal or sub-optimal. It can be seen that, the proposed model effectively improves the accuracy of foreground and background separation, and suppresses the interference of complex weather and target movement on foreground extraction.
陈利霞, 班颖, 王学文. 基于张量核范数与3D全变分的背景减除[J]. 计算机应用, 2020, 40(9): 2737-2742.
CHEN Lixia, BAN Ying, WANG Xuewen. Background subtraction based on tensor nuclear norm and 3D total variation. Journal of Computer Applications, 2020, 40(9): 2737-2742.
[1] 孙吉花. 背景减除的算法研究[D]. 长沙:国防科技大学,2006:1-10.(SUN J H. Research on algorithm of background subtraction[D]. Changsha:National University of Defense Technology,2006:1-10.) [2] ZHOU X,YANG C,ZHAO H,et al. Low-rank modeling and its applications in image analysis[J]. ACM Computing Surveys,2014, 47(2):No. 36. [3] LIN Z,CHEN M,MA Y. The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices[J]. Journal of Structural Biology,2010,181(2):116-127. [4] 陈利霞, 刘俊丽, 王学文. 结合加权Schatten-p范数与3D全变分的前景检测[J]. 计算机应用,2019,39(4):1170-1175.(CHEN L X,LIU J L,WANG X W. Foreground detection with weighted Schatten-p norm and 3D total variation[J]. Journal of Computer Applications,2019,39(4):1170-1175.) [5] GOLDFARB D,QIN Z. Robust low-rank tensor recovery:models and algorithms[J]. SIAM Journal on Matrix Analysis and Applications,2014,35(1):225-253. [6] XIE Q,ZHAO Q,MENG D,et al. Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018,40(8):1888-1902. [7] LIU Y, CHEN L, ZHU C. Improved robust tensor principal component analysis via low-rank core matrix[J]. IEEE Journal of Selected Topics in Signal Processing,2018,12(6):1378-1389. [8] LU C, FENG J, CHEN Y, et al. Tensor robust principal component analysis with a new tensor nuclear norm[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2020, 42(4):925-938. [9] YUAN M,LIN Y. Model selection and estimation in regression with grouped variables[J]. Journal of the Royal Statistical Society:Series B(Statistical Methodology),2006,68(1):49-67. [10] XU Y,WU Z,CHANUSSOT J,et al. Joint reconstruction and anomaly detection from compressive hyperspectral images using Mahalanobis distance regularized tensor RPCA[J]. IEEE Transactions on Geoscience and Remote Sensing,2018,56(6):2919-2930. [11] CAO W,WANG Y,SUN J,et al. Total variation regularized tensor RPCA for background subtraction from compressive measurements[J]. IEEE Transactions on Image Processing, 2016,25(9):4075-4090. [12] WANG Y,YIN W,ZENG J. Global convergence of ADMM in non-convex non-smooth optimization[J]. Journal of Scientific Computing,2019,78(1):29-63. [13] BOYD S,PARIKH N,CHU E,et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning, 2011,3(1):1-122. [14] TOM A J, GEORGE S N. Tensor total variation regularized moving object detection for surveillance videos[C]//Proceedings of the 2018 International Conference on Signal Processing and Communications. Piscataway:IEEE,2018:327-331. [15] WANG Y,JODOIN P M,PORIKLI F,et al. CDnet 2014:an expanded change detection benchmark dataset[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway:IEEE,2014:393-400. [16] 刘仲民, 何胜皎, 胡文瑾, 等. 基于背景减除法的视频序列运动目标检测[J]. 计算机应用,2017,37(6):1777-1781.(LIU Z M,HE S J,HU W J,et al. Moving object detection based on background subtraction for video sequence[J]. Journal of Computer Applications,2017,37(6):1777-1781.)
2020, Vol. 40 
