摘要
传统的鲁棒主成分分析模型能较好地解决视频前景检测问题.但是,若该模型的假设条件不能满足,算法性能会变差.针对此问题,本文提出了一种低秩与加权稀疏分解模型,通过对前景矩阵加权以增强其稀疏性.在建立加权矩阵的过程中,采用光流法获取每帧的运动矢量,以区分真实运动区域.其次,进一步提出一种增强模型,通过将加权矩阵作用于观测矩阵及背景矩阵,防止前景与背景的错误分离.实验结果表明,在无噪和有噪的情况下,提出的算法均能有效地分离监控视频中的前景和背景.
The traditional robust principal component analysis（ RPCA） model is able to solve the video foreground detection problem well. However,if the basic assumptions are violated,this model will have poor performance. This paper proposes a lowrank and reweighted sparse decomposition model,where the foreground matrix is reweighted so as to enhance its sparsity. When the weighting matrix is established,the optical flowmethod is used to get the motion vectors in each frame in order that the real moving areas can be recognized. Afterwards,based on the newly proposed model,an enhanced decomposition model is also developed. Since the weighting matrix is applied to both the observation matrix and the background matrix,the enhanced model is able to prevent the foreground and the background from being wrongly separated. Experimental results showthat the proposed algorithm can efficiently separate foreground and background components for video clips with or without noises.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2017年第9期2272-2280,共9页
Acta Electronica Sinica
基金
国家自然科学基金(No.61401108)
广西自然科学基金(No.2016GXNSFAA380154)
关键词
前景检测
运动目标检测
鲁棒主成分分析
低秩表示
光流法
foreground detection
moving object detection
robust principal component analysis（RPCA）
low-rank representation
optical flow method
