Background Subtraction under Sudden Illumination Changes

Abstract

Robust background subtraction under sudden illumination changes is a challenging problem. In this paper, we propose an approach to address this issue, which combines the Eigenbackground algorithm together with a statistical illumination model. The first algorithm is used to give a rough reconstruction of the input frame, while the second one improves the foreground segmentation. We introduce an online spatial likelihood model by detecting reliable background and foreground pixels. Experimental results illustrate that our approach achieves consistently higher accuracy compared to several state-of-the-art algorithms.