计算机工程 ›› 2019, Vol. 45 ›› Issue (1): 270-277.doi: 10.19678/j.issn.1000-3428.0049186

• 图形图像处理 • 上一篇    下一篇

Welsch M-估计在视频重构与背景减除中的应用

夏森林,孙怀江,陈贝佳   

  1. 南京理工大学 计算机科学与工程学院,南京 210094
  • 收稿日期:2017-11-06 出版日期:2019-01-15 发布日期:2019-01-15
  • 作者简介:夏森林(1993—),男,硕士研究生,主研方向为图像处理;孙怀江,教授、博士、博士生导师;陈贝佳,硕士研究生。
  • 基金项目:

    国家自然科学基金(61772272)

Application of Welsch M-estimation in Video Reconstruction and Background Subtraction

XIA Senlin,SUN Huaijiang,CHEN Beijia   

  1. School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
  • Received:2017-11-06 Online:2019-01-15 Published:2019-01-15

摘要:

针对监控视频在压缩采样过程中混入冲击噪声后的背景减除问题,提出一种基于Welsch M-估计与张量分解正则化的鲁棒视频重构与分解模型。为削弱冲击噪声对重构性能的影响,引入Welsch M-估计替代均方差作为衡量重建误差的代价函数。在张量框架下,将背景在不同维度、不同场景下的低秩差异性先验引入背景建模,得到重构与分解模型,并基于半二次理论和多块交替方向乘子方法给出相应的优化求解算法。实验结果表明,与SpaRCS、CS-L1PCA等算法相比,该算法在混入冲击噪声情况下,仍能保持视频重构与分解的鲁棒性。

关键词: 压缩感知, 冲击噪声, 背景减除, Welsch M-估计, 张量分解, 半二次理论

Abstract:

Aiming at the background subtraction problem of surveillance video mixed with impact noise during compression sampling,a robust video reconstruction and decomposition model based on Welsch M-estimation and tensor decomposition regularization is proposed.In order to reduce the impact of effect noise on reconstruction performance,Welsch M-estimation is used to replace the mean square error as a cost function to measure the reconstruction error,and a more robust reconstruction model is constructed.Under the tensor framework,the low-rank difference priors of the background in different dimensions and different scenarios are introduced into the background modeling to obtain the reconstruction and decomposition model,and based on half-quadratic theory and multi-block ADMM method,the corresponding optimization algorithm is given.Experimental results show that compared with the algorithms such as SpaRCS and CS-L1PCA,the algorithm can maintain the robustness of video reconstruction and decomposition under the condition of mixed impact noise.

Key words: Compressive Sensing(CS), impact noise, background subtraction, Welsch M-estimation, tensor decomposition, half-quadratic theory

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