计算机与数字工程2025，Vol.53Issue(4)：1025-1030,6.DOI:10.3969/j.issn.1672-9722.2025.04.019
基于半阈值算子的RPCA模型及应用
RPCA Model and Application Based on Half Threshold Operator
陈文松 1丁咏梅 1伍世虔2
作者信息
- 1. 武汉科技大学理学院 武汉 430070
- 2. 武汉科技大学机器人与人工智能研究院 武汉 430081
- 折叠
摘要
Abstract
Foreground-background separation in video is one of the important tasks of video surveillance system.Its purpose is to separate moving objects in the foreground from the video background.The robust principal component analysis(RPCA)algo-rithm based on principal component tracking(PCP)can not approach the rank function well because its nuclear norm is a biased es-timate of the rank function,and the sum of singular values is easily affected by individual values,resulting in sharp degradation of its separation performance and low accuracy in some complex scenarios.Therefore,this paper proposes a robust principal compo-nent analysis(NCRPCA)algorithm based on nonconvex function and half threshold operator.The nuclear norm of PCP algorithm is replaced by nonconvex function.Compared with the nuclear norm,nonconvex function is a more rigorous approximation of rank func-tion,and the approximation effect is better.In addition,in order to obtain more sparse and accurate solutions,the sparse terms are constrained by l1/2 norm.Finally,the proposed algorithm is applied to the experiments of foreground-background separation in stat-ic and dynamic video respectively,and the effectiveness and superiority of this algorithm are verified from the visual effect and quan-titative aspects.关键词
鲁棒主成分分析/l1/2范数/半阈值/前背景分离Key words
RPCA/l1/2 norm/half threshold/foreground-background separation分类
信息技术与安全科学引用本文复制引用
陈文松,丁咏梅,伍世虔..基于半阈值算子的RPCA模型及应用[J].计算机与数字工程,2025,53(4):1025-1030,6.
