Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (9): 2737-2742.DOI: 10.11772/j.issn.1001-9081.2020010005

• Virtual reality and multimedia computing • Previous Articles     Next Articles

Background subtraction based on tensor nuclear norm and 3D total variation

CHEN Lixia1,2, BAN Ying1,2, WANG Xuewen3   

  1. 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
  • Received:2020-01-15 Revised:2020-04-24 Online:2020-09-10 Published:2020-05-06
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (11961010), the Natural Science Foundation of Guangxi (2018GXNSFAA138169).


陈利霞1,2, 班颖1,2, 王学文3   

  1. 1. 桂林电子科技大学 数学与计算科学学院, 广西 桂林 541004;
    2. 广西高校数据分析与计算重点实验室(桂林电子科技大学), 广西 桂林 541004;
    3. 桂林电子科技大学 计算机与信息安全学院, 广西 桂林 541004
  • 通讯作者: 王学文
  • 作者简介:陈利霞(1979-),女,湖北浠水人,教授,博士,主要研究方向:数字图像处理中的数学理论与算法;班颖(1995-),女,河北廊坊人,硕士研究生,主要研究方向:数字图像处理中的数学理论与算法;王学文(1979-),男,湖北浠水人,讲师,硕士,主要研究方向:数字图像处理。
  • 基金资助:

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.

Key words: background subtraction, Tensor Robust Principal Component Analysis (TRPCA), tensor nuclear norm, 3D Total Variation (3D-TV), Alternating Direction Multiplier Method (ADMM)

摘要: 针对常用背景减除方法忽略前景时空连续性的问题,以及动态背景对前景提取的干扰问题,基于张量鲁棒主成分分析(TRPCA)提出了一种改进的背景减除模型。该模型利用改进的张量核范数对背景进行约束,加强了背景的低秩性,保留了视频的空间信息;然后用3D全变分(3D-TV)对前景进行正则化约束,考虑了目标在时空上的连续性,有效地抑制了动态背景和目标移动对前景提取造成的干扰。实验结果表明,所提算法能有效地分离视频中的前景和背景,且与高阶鲁棒主成分分析(HoRPCA)、带有新核范数的张量鲁棒主成分分析(TRPCA-TNN)和基于克罗内克基的鲁棒主成分分析(KBR-RPCA)等方法相比,综合评判指标F-measure值均处于最优或次优状态。由此可见,所提算法有效地提高了前景背景分离的准确度,抑制了复杂天气和目标移动对前景提取的干扰。

关键词: 背景减除, 张量鲁棒主成分分析, 张量核范数, 3D全变分, 交替方向乘子法

CLC Number: