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Motion segmentation in compressed video using Markov Random Fields

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3 Author(s)
Yue-Meng Chen ; Sch. of Eng. Sci., Simon Fraser Univ., Burnaby, BC, Canada ; Bajic, I.V. ; Saeedi, P.

In this paper, we propose an unsupervised segmentation algorithm for extracting moving objects/regions from compressed video using Markov Random Field (MRF) classification. First, motion vectors (MVs) are 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 superiority over state-of-the-art methods.

Published in:

Multimedia and Expo (ICME), 2010 IEEE International Conference on

Date of Conference:

19-23 July 2010