A multigrid approach for hierarchical motion estimation

This paper focuses on the estimation of the apparent motion field between two consecutive frames in an image sequence. The approach developed here is a trade-off between methods based on global parameterized flow models and local dense optic flow estimators. The method relies on an adaptive multigrid minimization approach. In addition to accelerated convergence toward good estimates, it allows to mix different parameterizations of the estimate relative to adaptive partitions of the image. The performances of the resulting algorithms are demonstrated in the difficult context of a noon-convex energy. Experimental results on real world Meteosat sequences are presented.