Summary: Dynamic background extraction has been a fundamental research topic in video analysis. In this paper, a novel robust principal component analysis (RPCA) approach for foreground extraction is proposed by decomposing video frames into three parts: rank-one static background, dynamic background and sparse foreground. First, the static background is represented by a rank-one matrix, which can avoid the computation of singular value decomposition because usually the dimensionality of a surveillance video is very large. Secondly, the dynamic background is characterized by \(\ell_{2,1}\)-norm, which can exploit the shared information. Thirdly, the sparse foreground is enhanced by total variation, which can preserve sharp edges that are usually the most important for clear object extraction. An efficient symmetric Gauss-Seidel based alternating direction method of multipliers (sGS-ADMM) is established with convergence analysis. Extensive experiments on real-world datasets show that our proposed approach outperforms the existing state-of-the-art approaches. In fact, to the best of our knowledge, this is the first time to integrate the joint sparsity and total variation into a RPCA framework, which has demonstrated the superiority of performance.