A Novel Detection Approach for Moving Object with Dynamic Background

Moving object detection from video data is one of the most active research points in computer vision area. Recently the
$\ell$
2,0
-norm has shown remarkable advantages in signal processing, and the total variation (TV) has been widely used in image processing due to its ability to preserve sharp edges, which are usually the most important for clear object extraction. In this paper, firstly, a unified regularization framework named
$\ell$
2,0
mixed norm and total variation regularized RPCA (MNTV-RPCA) is presented to extract the foreground, which is able to enhance the smoothness of boundary. Secondly, the alternating direction method of multipliers (ADMM) is employed to deal with the proposed constrained minimization problem. Experimental results show that our method have better performance than some state-of-the-art methods especially for the cases with dynamic backgrounds.