MRF-based motion segmentation exploiting a 2D motion model robust estimation

This paper deals with motion-segmentation, that is, with the partitioning of the image into regions of homogeneous motion. Here, homogeneous means that in each region a 2D polynomial model (e.g. an affine one) is able to describe at each location the underlying “true” motion with a predefined precision η. However, no estimation of this true motion field is required. The motion models are computed using a multiresolution robust estimator. Therefore, as opposed to almost all other motion-segmentation scheme, the motion model of a given region only needs to be estimated once at a given time instant. Moreover, the determination of the boundaries between the different regions, which is stated as a statistical regularization based on a multiscale Markov random field (MRF) modeling, only requires one pass. Finally, thanks to the definition of an explicit detection step of areas where the error between the underlying motion and the one given by the estimated models is not within the precision η, we are able to get a good segmentation from the very beginning of the sequence, and to manage the appearance of new objects in the scene, as well as the momentary increase in the complexity of motion in already existing regions. Results obtained on many real image sequences have validated our approach