FOREGROUND DETECTION USING LOW RANK AND STRUCTURED SPARSITY

In this paper, a novel foreground detection method based on two-stage framework is presented. In the first stage, a class of structured sparsity-inducing norms is introduced to model moving objects in videos and thus regard the observed sequence as being made up of the sum of a low-rank matrix and a structured sparse outlier matrix. In virtue of adaptive parameters, the proposed method includes a motion saliency measurement to dynamically estimate the support of the foreground in the second stage. Experiments on challenging datasets demonstrate that the proposed approach outperforms the state-of-the-art methods and works effectively on a wide range of complex videos.