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This paper proposes a new algorithm to segment moving objects from color sequences accurately. The segmentation procedure is treated as a Markovian labeling process and is formulated by a hierarchical Markov random field (MRF) model. Initially, the original frame is partitioned into homogeneous regions with different granularity by the rapid watershed algorithm. Then, the foreground is detected as outliers of the estimated background motion in the initial motion classification stage. After that, the motion vector is estimated for each foreground region and is validated by an elaborate occlusion detection scheme. The initial object mask is segmented by the MRF model on the larger-scale spatial partition and is refined by the other MRF model in the small-scale partition. The hierarchical MRF models provide the fine object boundary. The proposed method is evaluated on several real-world image sequences and the experimental results shows remarkable performance.