Background subtraction via online box constrained RPCA

Abstract

To address the issue of background subtraction include shadow challenge, an online robust principal component analysis (RPCA) method with box constraint (BC-RPCA) has been proposed to detect moving object and accelerate the RPCA like method. First of all, the BC-RPCA method considers the input image sequences as low rank background, sparse foreground and moving shadow. Then the Augmented Lagrangian method is used to convert the box constraint into the objective function and rank-1 modification for thin SVD is also employed to accelerate the solver via alternating direction method of multipliers (ADMM). Finally, the experiments demonstrated the proposed method works effectively and has low computational complexity during real-time application.