针对传统背景建模存在的问题，文中基于低秩矩阵恢复原理，直接从视频序列中分离出前景物体和背景模型。已有低秩矩阵恢复算法的迭代计算过程中涉及大量的奇异值分解，而这些奇异值分解一般非常耗时且不够简洁，文中在非精确增广拉格朗日乘子法中引入线性时间奇异值分解算法，以得到更加有效的背景建模算法。基于

实际视频序列实验，结果表明该改进算法具有更好的建模效果和较少的运算时间。-In this paper，a novel method is present based on low-rank matrix recovery，which can directly

obtain background model as well as foreground objects from the video sequence． As the main computation

of existing algorithms of low-rank matrix recovery is the singular value decomposition，most of which are

time-consuming and not concise enough，the linear time SVD algorithm is introduced to the inexact augmented

Lagrange multiplier method，and we get a more efficient background modeling algorithm． We test

our algorithm on real video，and the experimental results show that our approach obtains good results and

less time-consuming，compared to the exact and inexact augmented Lagrange multiplier method．