A Novel Moving Object Segmentation Technology Based on Spatiotemporal Markov Random Field

In the field of image processing, the segmentation of moving object in video sequences is a hot research topic in recent years. In this paper, a novel method of moving object segmentation based on spatiotemporal Markov Random Field(MRE) is proposed. Firstly, two observations and two initial labels are derived from the three successive images with the same method in the first scheme. Secondly, the AND-label is obtained with the AND-operation on the two initial labels. Finally, the image segmented with the color clustering algorithm is regarded as prior knowledge, with which the corresponding Gibbs energy function is redefined, and the maximum a posteriori estimator, which is determined by using the iterated conditional mode algorithm, is employed to get optimized labels. The new MRF model contributes to the weakening of the noise and to the elimination of the covered-uncovered background and to the recovery of the uniform moving regions.