Robust Dual-Graph Regularized Moving Object Detection

Moving object detection and its associated background-foreground separation have been widely used in a lot of applications, including computer vision, transportation and surveillance. Due to the presence of the static background, a video can be naturally decomposed into a low-rank background and a sparse foreground. Many regularization techniques, such as matrix nuclear norm, can therefore be imposed on the background. In the meanwhile, sparsity or smoothness based regularizations, such as total variation and $\ell_{1}$, can be imposed on the foreground. Moreover, graph Laplacians are further used to capture the complicated geometry of background images. Recently, weighted regularization techniques including the weighted nuclear norm regularization have been proposed in the image processing community to promote adaptive sparsity while achieving efficient performance. In this paper, we propose a robust dual-graph regularized moving object detection model based on a new weighted nuclear norm regularization and spatiotemporal graph Laplacians, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on realistic body movement data sets have demonstrated the effectiveness of this method in separating moving objects from background, and the great potential in robotic applications.