Region-level motion-based background modeling and subtraction using MRFs

Shih Shinh Huang*, Li Chen Fu, Pei Yung Hsiao

*此作品的通信作者

研究成果: 期刊稿件文章同行評審

67 引文 斯高帕斯(Scopus)

摘要

This paper presents a new approach to automatic segmentation of foreground objects 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 classification problem as a graph labeling over a region adjacency graph 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, a temporal coherence is also maintained by modeling it as the prior energy. On the other hand, color distributions of two neighboring regions are taken into consideration to impose spatial coherence. Then, the a priori energy of MRFs takes both spatial and temporal coherence into account to maintain the continuity of our segmentation. Finally, a labeling is obtained by maximizing the a posteriori energy of the MRFs. Under such formulation, we integrate two different kinds of techniques in an elegant way to make the foreground detection more accurate. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.

原文英語
頁(從 - 到)1446-1456
頁數11
期刊IEEE Transactions on Image Processing
16
發行號5
DOIs
出版狀態已出版 - 05 2007
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