In this paper

we propose an algorithm named Multi-feature Robust Principal Component Analysis(MFRPCA)

to integrate multiple visual features for Video Moving Object Segmentation. The aim of Video Moving Object Segmentation is to separate the moving objects from the static information. Its main process is to decompose the multiple feature video matrices into low rank and sparse matrices. The decomposition is to solve a minimization problem formulated as a constrained combination of nuclear norm and Lnorm

which can be solved efficiently by Augmented Lagrange Multiplier(ALM) method. Compared to other methods developed recently

the proposed method integrates color

edge and texture features. The quantitative results for Recall and F-measure obtained from experiments on the change detection benchmark dataset are 0.486 0 and 0.559 7 respectively. The results which outperform other methods well show that the proposed MFRPCA can achieve more robust and reliable performance.