摘要
针对传统鲁棒主成分分析(RPCA)秩函数和稀疏度函数逼近程度不够高的问题,给出一种新的基于分数范数与最小最大凹罚(MCP)函数的运动目标检测算法。首先通过分数范数与最小最大凹罚函数分别逼近秩函数和稀疏度函数,以实现对低秩部分和稀疏部分的更佳逼近,从而提取出更优的运动目标;然后使用交替方向乘子法(ADMM)求解提出的算法;最后为了体现该算法的优势,通过仿真实验得到其平均F-measure值为0.69576。与其他鲁棒主成分分析算法相比,该算法的运动目标检测效果更好,相较于性能最好的对比算法Godec的F-measure值大约提高了17%。
For robust principal component analysis(RPCA)algorithm based on traditional rank function and sparsity function approximation is not high enough,present a new moving object detection based on the fractional norm and minimax concave penalty(MCP).Firstly,the rank and sparsity functions are approximated by the fractional norm and the minimax concave penalty function to achieve a better approximation of the low-rank and the sparse parts respectively,so as to extract a better moving object.Then,the alternating direction method of multipliers(ADMM)is used to solve the proposed model.Finally to reflect the advantages of the proposed method,through simulation experiment,the average F-measure value of the proposed method is 0.69576.Compared with other RPCA-based method,the effect is better,and compared with the best performance comparison algorithm,F-measure value increases approximately 17%.
作者
吕佳辉
刘欣昕
张洪瑞
杨永鹏
杨真真
LYU Jia-hui;LIU Xin-xin;ZHANG Hong-rui;YANG Yong-peng;YANG Zhen-zhen(School of Science,Nanjing University of Posts and Telecommunications;School of Network and Communication,Nanjing Vocational College of Information Technology,Nanjing 210023,China)
出处
《软件导刊》
2023年第3期174-178,共5页
Software Guide
基金
南京邮电大学宽带无线通信与传感网技术教育部重点实验室开放研究基金项目(JZNY202113)
南京邮电大学科研项目(NY220207)
南京邮电大学教学改革研究项目(JG00720JX51)
大学生创新创业训练计划项目(SYB2021033)。
关键词
非凸鲁棒主成分分析
最小最大凹罚
分数范数
交替方向乘子法
运动目标检测
nonconvex robust principal component analysis
minimax concave penalty
fractional norm
alternating direction method of multipliers
moving object detection
