DENSE MOTION ESTIMATION BETWEEN DISTANT FRAMES: COMBINATORIAL MULTI-STEP INTEGRATION AND STATISTICAL SELECTION

Accurate estimation of dense point correspondences between two distant frames of a video sequence is a challenging task. To address this problem, we present a combinatorial multi-step integration procedure which allows one to obtain a large set of candidate motion fields between the two distant frames by considering multiple motion paths across the video se-quence. Given this large candidate set, we propose to perform the optimal motion vector selection by combining a global op-timization stage with a new statistical processing. Instead of considering a selection only based on intrinsic motion field quality and spatial regularization, the statistical processing exploits the spatial distribution of candidates and introduces an intra-candidate quality based on forward-backward consis-tency. Experiments evaluate the effectiveness of our method for distant motion estimation in the context of video editing. Index Terms — motion estimation, statistical analysis, dense point matching, distant frames 1