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Dense Lagrangian motion estimation with occlusions

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Dense Lagrangian motion estimation with occlusions
We couple occlusion modeling and multi-frame motion estimation to compute dense, temporally extended point trajectories in video with significant occlusions. Our approach combines robust spatial regularization with spatially and temporally global occlusion labeling in a variational, Lagrangian framework with subspace constraints. We track points even through ephemeral occlusions. Experiments demonstrate accuracy superior to the state of the art while tracking more points through more frames.
Susanna Ricco, Carlo Tomasi
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Susanna Ricco, Carlo Tomasi
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