Robust Foreground Segmentation Using Subspace Based Background Model

Robust foreground segmentation is an essential step in many computer vision applications such as visual surveillance and behavior analysis. This paper proposes a subspace based background modeling and foreground segmentation algorithm, which improves the incremental background subspace learning in a robust manner. It can efficiently reduce the influence of the foreground pixels which are undesired in background updating procedure, at the same time, adapts well to background variations. Furthermore, a novel subspace initialization method based on L¹-minimization is proposed to efficiently construct the subspace background model using global information, without the requirement of empty scene. Experimental results demonstrate the robustness and effectiveness of the algorithm.