Moving object detection method based on low rank-sparse and total variational representation

Moving object detection with dynamic background is addressed in this paper. New method of moving object detection with low rank-sparse and total variation representation is proposed. The proposed method is based on robust principal component analysis (RPCA), and the three-dimensional total variation is constrained to the moving object. Then the interference of the dynamic background is removed. At the same time, the group sparsity of the coefficients of the low rank matrix in the orthogonal subspace is used to accelerate the computation of rank minimization of the low rank matrix, which compensate for the large amount of total variational computation and balance the overall running speed. Experimental results show that the method can not only detect the moving objects in complex background, but also maintain fast running speed.