Authors:
Pierre-Henri Conze
1
;
Philippe Robert
2
;
Tomás Crivelli
2
and
Luce Morin
3
Affiliations:
1
Technicolor and UEB, France
;
2
Technicolor, France
;
3
UEB, France
Keyword(s):
Long-term Motion Estimation, Dense Point Matching, Statistical Analysis, Long-term Trajectories, Video Editing.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Tracking and Visual Navigation
Abstract:
We present statistical multi-step flow, a new approach for dense motion estimation in long video sequences.
Towards this goal, we propose a two-step framework including an initial dense motion candidates generation
and a new iterative motion refinement stage. The first step performs a combinatorial integration of elementary
optical flows combined with a statistical candidate displacement fields selection and focuses especially on
reducing motion inconsistency. In the second step, the initial estimates are iteratively refined considering
several motion candidates including candidates obtained from neighboring frames. For this refinement task,
we introduce a new energy formulation which relies on strong temporal smoothness constraints. Experiments
compare the proposed statistical multi-step flow approach to state-of-the-art methods through both quantitative
assessment using the Flag benchmark dataset and qualitative assessment in the context of video editing.