Over-Segmentation Based Background Modeling and Foreground Detection with Shadow Removal by Using Hierarchical MRFs

In this paper, we propose a novel over-segmentation based method for the detection of foreground objects from a surveillance video by integrating techniques of background modeling and Markov Random Fields classification. Firstly, we introduce a fast affinity propagation clustering algorithm to produce the over-segmentation of a reference image by taking into account color difference and spatial relationship between pixels. A background model is learned by using Gaussian Mixture Models with color features of the segments to represent the time-varying background scene. Next, each segment is treated as a node in a Markov Random Field and assigned a state of foreground, shadow and background, which is determined by using hierarchical belief propagation. The relationship between neighboring regions is also considered to ensure spatial coherence of segments. Finally, we demonstrate experimental results on several image sequences to show the effectiveness and robustness of the proposed method.