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.
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