MOTION SEGMENTATION BY TRACKING EDGE INFORMATION OVER MULTIPLE FRAMES
Paul Smith, Tom Drummond, and Roberto Cipolla
This paper presents a new Bayesian framework for layered motion segmentation, dividing the frames of an image sequence into foreground and background layers by tracking edges. The first frame in the sequence is segmented into regions using image edges, which are tracked to estimate two affine motions. The probability of the edges fitting each motion is calculated using 1st order statistics along the edge. The most likely region labelling is then resolved using these probabilities, together with a Markov Random Field prior. As part of this process one of the motions is also identified as the foreground motion.
Good results are obtained using only two frames for segmentation. However, it is also demonstrated that over multiple frames the probabilities may be accumulated to provide an even more accurate and robust segmentation. The final region labelling can be used, together with the two motion models, to produce a good segmentation of an extended sequence.
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