This paper presents a new approach to automatic segmentation of the foreground objects from the sequence of images by integrating techniques of background subtraction and motion-based segmentation. At first, a background model is built to represent information of both color and motion of the background scene. Based on temporal and spatial information, an initial partition of each image is obtained. Next, we formulate the classification problem as a graph labeling over a region adjacency graph (RAG) based on Markov random fields (MRFs) statistical framework. The Bhattacharyya distance for estimating the similarity between color and motion distributions of the background model and the currently obtained regions are used to model the likelihood energies. The object tracking strategy for finding the correspondence between region at different time instant is used to maintain the temporal coherence of the segmentation. For spatial coherence, the length of the common boundaries of two regions is taken into consideration for classification. Both spatial and temporal coherence are incorporated into the prior energy to maintain the continuity of the segmentation. Finally, a labeling is obtained by maximizing a posterior probability of the MRFs. Under such formulation, we integrate two different kinds of framework in an elegant way to make the foreground detection become more accurate. Experimental results for two image sequences including the hall monitoring and our e-home demo site are provided to demonstrate the effectiveness of the proposed approach.
['Shih-Shinh Huang', 'Li-Chen Fu', 'Pei-Yung Hsiao']
A Region-Level Motion-Based Background Modeling and Subtraction Using MRFs