张海坤1
1.大连大学信息工程学院 大连 116622
2.浙江理工大学信息学院 杭州 310018
基金项目:国家自然科学基金(61301258, 61271379),中国博士后科学基金(2016M590218),重点实验室基金(61424010106),河南省高等学校重点科研项目支持计划(14A520079),河南省科技攻关计划(162102210168)
详细信息
作者简介:王洪雁:男,1979年生,副教授,博士,主要研究方向为MIMO雷达信号处理,毫米波通信,机器视觉
张海坤:男,1995年生,硕士生,主要研究方向为图像处理,计算机视觉
通讯作者:王洪雁 gglongs@163.com
中图分类号:TN911.73; TP391计量
文章访问数:572
HTML全文浏览量:238
PDF下载量:68
被引次数:0
出版历程
收稿日期:2019-06-20
修回日期:2020-04-20
网络出版日期:2020-08-29
刊出日期:2020-11-16
Moving Object Detection Method Based on Low-Rank and Sparse Decomposition in Dynamic Background
Hongyan WANG1, 2,,,Haikun ZHANG1
1. College of Information Engineering, Dalian University, Dalian 116622, China
2. School of Information Science and technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
Funds:The National Natural Science Foundation of China (61301258, 61271379), China Postdoctoral Science Foundation (2016M590218), The Key Laboratory Foundation (61424010106), The Henan Province Support Plans for Key Scientific Research Projects of Colleges and Universities (14A520079), The Henan Province Plans for Science and Technology Development (162102210168)
摘要
摘要:针对背景运动引起动目标检测精度显著下降的问题,该文提出一种基于低秩及稀疏分解的动目标检测方法。所提方法首先引入伽马范数(
![](http://jeit.ac.cn/cn/article/doi/10.11999/190452_M1.jpg)
![](http://jeit.ac.cn/cn/article/doi/10.11999/190452_M2.jpg)
关键词:前景检测/
动态背景/
低秩/
稀疏/
L1/2正则化
Abstract:Focusing on the issue that the detection accuracy of moving object is significantly reduced by background motion, a low-rank and sparse decomposition based moving object detection method is developed. Firstly, in order to solve the problem that the nuclear norm over-penalizing large singular values lead to the optimal solution of the obtained minimization problem can not be obtained and then the detection performance is decreased, the gamma norm (
![](http://jeit.ac.cn/cn/article/doi/10.11999/190452_M3.jpg)
![](http://jeit.ac.cn/cn/article/doi/10.11999/190452_M4.jpg)
Key words:Foreground detection/
Dynamic background/
Low-rank/
Sparsity/
L1/2 regularization
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https://jeit.ac.cn/article/exportPdf?id=8ce80d20-6f71-4ac2-b564-7c352f13f754