This paper presents an algorithm for the automatic segmentation of indoor videos into foreground and background
layers. Segmenting foreground from an indoor video with local foreground motion and illumination changes is
challenging. We first detect key frames with reliable motion using nonparametric model in chromaticity space. From
these key frames, we learn an appearance model as a color degenerating model. Robust indoor video segmentation is
achieved by combining these learned color and structure cues in a Markov random field framework. Experimental results
on different sequences demonstrate the effectiveness of our algorithm.