Moving Region Segmentation From Compressed Video Using Global Motion Estimation and Markov Random Fields

Abstract

In this paper, we propose an unsupervised segmentation algorithm for extracting moving regions from compressed video using global motion estimation (GME) and Markov random field (MRF) classification. First, motion vectors (MVs) are compensated from global motion and quantized into several representative classes, from which MRF priors are estimated. Then, a coarse segmentation map of the MV field is obtained using a maximum a posteriori estimate of the MRF label process. Finally, the boundaries of segmented moving regions are refined using color and edge information. The algorithm has been validated on a number of test sequences, and experimental results are provided to demonstrate its advantages over state-of-the-art methods.