Robust estimation of foreground in surveillance videos by sparse error estimation

Frames of videos with static background and dynamic foreground can be viewed as samples of signals that vary slowly in time with sparse corruption caused by foreground objects. We cast background subtraction as a signal estimation problem, where the error sparsity is enforced through minimization of the L₁ norm of the difference between the processed frame and estimated background subspace, as an approximation to the underlying L₀ norm minimization structure. Our work provides a novel framework for background subtraction with the added benefit of easy integration of local discriminative information (e.g. gradient, texture, motion field etc.) for improved robustness. We show that the proposed method is able to overcome various difficulties frequently encountered in real application settings, and is competitive with the state of the art.