Abstract
This paper presents a new approach to automatic segmentation of foreground objects with shadow removal from an image sequence by integrating techniques of background subtraction and motion-based foreground segmentation. First, a region-based motion segmentation algorithm is proposed to obtain a set of motion-coherence regions and the correspondence among regions at different time instants. Next, we formulate the foreground detection problem as a graph labeling over a region adjacency graph (RAG) based on Markov random fields (MRFs) statistical framework. A background model representing the background scene is built and then is used to model a likelihood energy. Besides the background model, the temporal and spatial coherence are also maintained by modeling it as a prior energy. Finally, a labeling is obtained by maximizing a posterior energy of the MRFs. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.
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Huang, SS., Fu, LC., Hsiao, PY. (2006). Region-Level Motion-Based Foreground Detection with Shadow Removal Using MRFs. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_88
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DOI: https://doi.org/10.1007/11612032_88
Publisher Name: Springer, Berlin, Heidelberg
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