Abstract: In the method of robust principal component analysis to detection of moving objects, rank function of background matrix is often substituted by the nuclear norm. As a result, the threshold of shrinkage operator of singular values to seek low rank matrix is invariant and the precision of background recovery is relatively low. A new loss function composed of weighted nuclear norm and structured sparsity norm is proposed and optimized by alternating direction multiplier method. In the function, the weighted nuclear norm is adopted as a low rank constraint. It causes that the shrinkage threshold is monotonically decreasing with the corresponding singular value and thus these larger ones are shrunk in a small margin. The structured sparsity norm is used as a sparsity constraint of foreground matrix and it effectively utilizes prior knowledge of the regional continuity of foreground moving objects. The experimental results show that the proposed method can obtain better performances than other robust principal component analysis methods in dynamic background, shadow and other complex scenes.