Dense Lagrangian motion estimation with occlusions

Published

Journal Article

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. © 2012 IEEE.

Full Text

Duke Authors

Cited Authors

  • Ricco, S; Tomasi, C

Published Date

  • October 1, 2012

Published In

Start / End Page

  • 1800 - 1807

International Standard Serial Number (ISSN)

  • 1063-6919

Digital Object Identifier (DOI)

  • 10.1109/CVPR.2012.6247877

Citation Source

  • Scopus