Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (4): 1170-1175.DOI: 10.11772/j.issn.1001-9081.2018092038

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Foreground detection with weighted Schatten-p norm and 3D total variation

CHEN Lixia1, LIU Junli1, WANG Xuewen2   

  1. 1. College of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin Guangxi 541000, China;
    2. College of Computer and Information Security, Guilin University of Electronic Technology, Guilin Guangxi 541000, China
  • Received:2018-10-09 Revised:2018-12-02 Online:2019-04-10 Published:2019-04-10
  • Supported by:
    This work is partially supported by the Innovation Project of GUET Graduate Education (2017YJCX84), the Guangxi Higher Education Undergraduate Teaching Reform Project (2017JGB230).


陈利霞1, 刘俊丽1, 王学文2   

  1. 1. 桂林电子科技大学 数学与计算科学学院, 广西 桂林 541000;
    2. 桂林电子科技大学 计算机与信息安全学院, 广西 桂林 541000
  • 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;刘俊丽(1992-),女,河南周口人,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: In view of the fact that the low rank and sparse methods generally regard the foreground as abnormal pixels in the background, which makes the foreground detection precision decrease in the complex scene, a new foreground detection method combining weighted Schatten-p norm with 3D Total Variation (3D-TV) was proposed. Firstly, the observed data were divided into low rank background, moving foreground and dynamic disturbance. Then 3D total variation was used to constrain the moving foreground and strengthen the prior consideration of the spatio-temporal continuity of the foreground objects, effectively suppressing the random disturbance of the anomalous pixels in the discontinuous dynamic background. Finally, the low rank performance of video background was constrained by weighted Schatten-p norm to remove noise interference. The experimental results show that, compared with Robust Principal Component Analysis (RPCA), Higher-order RPCA (HoRPCA) and Tensor RPCA (TRPCA), the proposed model has the highest F-measure value, and the optimal or sub-optimal values of recall and precision. It can be concluded that the proposed model can better overcome the interference in complex scenes, such as dynamic background and severe weather, and its extraction accuracy as well as visual effect of moving objects is improved.

Key words: low-rank and sparse decomposition, foreground detection, weighted Schatten-p norm, 3D total variation

摘要: 针对低秩与稀疏方法一般将前景看作背景中存在的异常像素点,从而使得在复杂场景中前景检测精确度下降的问题,提出一种结合加权Schatten-p范数与3D全变分(3D-TV)的前景检测模型。该模型首先将观测数据三分为低秩背景、运动前景和动态干扰;然后利用3D全变分来约束运动前景,并加强对前景目标时空连续性的先验考虑,有效抑制了不连续动态背景异常点的随机扰动;最后利用加权Schatten-p范数约束视频背景的低秩性能,去除噪声干扰。实验结果表明,与鲁棒主成分分析(RPCA)、高阶RPCA(HoRPCA)和张量RPCA(TRPCA)等模型相比,所提模型的综合衡量指标F-measure值是最高的,查全率与查准率也处于最优或次优状态。由此可知,所提模型在动态背景、恶劣天气等复杂场景中能有效提高运动目标的提取精确度,且提取的前景目标视觉效果较好。

关键词: 低秩稀疏分解, 前景检测, 加权Schatten-p范数, 3D全变分

CLC Number: