Local Grouping for Optical Flow

Abstract

Optical flow estimation requires spatial integration, which essentially poses a grouping question: what points belong to the same motion and what do not. Classical local approaches to optical flow, such as Lucas-Kanade, use isotropic neighborhoods and have considerable difficulty near motion boundaries. In this work we utilize image-based grouping to facilitate spatial- and scale-adaptive integration. We define soft spatial support using pairwise affinities computed through intervening contour. We sample images at edges and corners, and iteratively estimate affine motion at sample points. Figure-ground organization further improves grouping and flow estimation near boundaries. We show that affinity-based spatial integration enables reliable flow estimation and avoids erroneous motion propagation from and/or across object boundaries. We demonstrate our approach on the Middlebury flow dataset.