The Aggregation Framework for Optical Flow Estimation
Denis Fortun

Seminar • 17 April 2014 • BM 4.233

Abstract
Optical flow estimation methods are usually divided in two main classes, regarding the local or global extent of the spatial coherency constraint imposed on the motion field. Local parametric approaches have clearly been outperformed by global regularized models, due to the difficulty to assess an appropriate local domain for parametric motion estimation. In this talk, we present a generic aggregation paradigm addressing this problem, based on purely local candidates estimations, combined in a subsequent global aggregation step. Based on this versatile framework, we address several issues. Two aggregation methods are presented, the first operating in a discrete framework with move-making graph cuts, and the second performing variational optimization of a sparsity constrained combination of candidates. In each case, no motion segmentation is required and multi-resolution schemes are avoided. The locality of regularized models is exploited with a variational computation of candidates, and we experimentally demonstrate that locally affine estimations are sufficient to produce highly accurate candidates. Finally the aggregation model is also adapted to handle two major issues of motion estimation, namely Illumination changes and occlusions. The performance of this approach is demonstrated on standard computer vision benchmarks, and it is shown to be particularly adapted to solve specific problems occurring in fluorescence imaging.