Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (4): 1170-1175.

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

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).

### 结合加权Schatten-p范数与3D全变分的前景检测

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:
桂林电子科技大学研究生科研创新项目（2017YJCX84）；广西高等教育本科教学改革工程项目（2017JGB230）。

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.

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