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 low rank 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 flow method 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 show that the proposed algorithm can efficiently separate foreground and background components for video clips with or without noises.