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Robust optical flow estimation based on a sparse motion trajectory set.

Authors
  • Gibson, David
  • Spann, Michael
Type
Published Article
Journal
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Publication Date
Jan 01, 2003
Volume
12
Issue
4
Pages
431–445
Identifiers
DOI: 10.1109/TIP.2003.811628
PMID: 18237921
Source
Medline
License
Unknown

Abstract

This paper presents an approach to the problem of estimating a dense optical flow field. The approach is based on a multiframe, irregularly spaced motion trajectory set, where each trajectory describes the motion of a given point as a function of time. From this motion trajectory set a dense flow field is estimated using a process of interpolation. A set of localized motion models are estimated, with each pixel labeled as belonging to one of the motion models. A Markov random field framework is adopted, allowing the incorporation of contextual constraints to encourage region-like structures. The approach is compared with a number of conventional optical flow estimation algorithms taken over a number of real and synthetic sequences. Results indicate that the method produces more accurate results for sequences with known ground truth flow. Also, applying the method to real sequences with unknown flow results in lower DFD, for all of the sequences tested.

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