计算机应用与软件2026，Vol.43Issue(5)：133-140,8.DOI:10.3969/j.issn.1000-386x.2026.05.018
一种基于SCP范数和结构稀疏的前景检测
A FOREGROUND DETECTION BASED ON SCP NORM AND STRUCTURE SPARSENESS
摘要
Abstract
Based on the robust principal component analysis(RPCA)model,aimed at the problems that the singular values are over-penalized and the spatial continuity of foreground is ignored in traditional RPCA models,a foreground detection model based on Schatten-Capped-P(SCP)norm and structure sparseness is proposed.The SCP norm was applied to constrain low-rank backgrounds which imposed different penalties on different singular values and could effectively avoid excessive punishment of singular values.The sparse foreground was constrained by the C(2,1)norm which took full use of the spatial continuity of the prospect.Experiments show that,compared with the five recent mainstream algorithms,the proposed model can effectively deal with the problems of excessive punishment of singular values and the spatial continuity of foreground.The proposed algorithm achieves the best average F-measure value.Compared with the suboptimal model,the average F-measure of the proposed model is increased by 44%in terms of per-formance.关键词
鲁棒主成分分析模型/SCP范数/C(2,1)范数/前景检测Key words
RPCA models/SCP norm/C(2,1)norm/Foreground detection分类
信息技术与安全科学引用本文复制引用
陆游尤,陈利霞..一种基于SCP范数和结构稀疏的前景检测[J].计算机应用与软件,2026,43(5):133-140,8.基金项目
国家自然科学基金项目(11961010) （11961010）
广西自然科学基金项目(2018GXNSFAA138169) （2018GXNSFAA138169）
桂林电子科技大学2022年院级研究生创新项目(2022YJSCX03). （2022YJSCX03）
