Dense Long-term Motion Estimation via Statistical Multi-step Flow
english

Pierre-Henri Conze, Philippe Robert, Tomás Crivelli, Luce Morin
2014 Proceedings of the 9th International Conference on Computer Vision Theory and Applications  
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
more » ... tep, 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.
doi:10.5220/0004683005450554 dblp:conf/visapp/ConzeRCM14 fatcat:2tauctftlvho3hopqqxpheoqfi