A Multigrid Approach for Hierarchical Motion Estimation

Abstract

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 tradeoff 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 non-convex energy. Experimental results on real world Meteosat sequences are presented. 1 Introduction Energy-based models constitute a powerful way to cope with low-level image processing problems. These methods are issued either from continuous formalism such as PDEs or from discrete modelization such as Markov random fields. When the energy is convex one can seek the unique minimum with deterministic descen..