From optical flow to dense long term correspondences

Dense point matching and tracking in image sequences is an open issue with implications in several domains, from content analysis to video editing. We observe that for long term dense point matching, some regions of the image are better matched by concatenation of consecutive motion vectors, while for others a direct long term matching is preferred. We propose a method to optimally estimate the correspondence of a point w.r.t. a reference image from a set of input motion estimations over different temporal intervals. Results on texture insertion by point tracking in the context of video editing are presented and compared with a state-of-the-art approach.