Dense motion estimation between distant frames: combinatorial multi-step integration and statistical selection

Pierre-Henri Conze (1, 2) , Tomas Crivelli (3) , Philippe Robert (3) , Luce Morin (2)

1 Technicolor [Cesson Sévigné]
2 IETR - Institut d'Électronique et des Technologies du numéRique
3 Technicolor Imaging Science Lab

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 multistep 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 sequence. Given this large candidate set, we propose to perform the optimal motion vector selection by combining a global optimization 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 consistency. Experiments evaluate the effectiveness of our method for distant motion estimation in the context of video editing.

Mots clés: motion estimation; statistical analysis; dense point matching; distant frames

Domaines: Traitement des images [eess.IV]

Pierre-Henri Conze, Tomas Crivelli, Philippe Robert, Luce Morin. Dense motion estimation between distant frames: combinatorial multi-step integration and statistical selection. IEEE International Conference on Image Processing, Sep 2013, Melbourne, Australia. pp.3429. ⟨hal-00868251⟩