On analyzing video with very small motions

Michael Dixon, Austin Abrams, Nathan Jacobs, Robert Pless

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


We characterize a class of videos consisting of very small but potentially complicated motions. We find that in these scenes, linear appearance variations have a direct relationship to scene motions. We show how to interpret appearance variations captured through a PCA decomposition of the image set as a scene-specific non-parametric motion basis. We propose fast, robust tools for dense flow estimates that are effective in scenes with small motions and potentially large image noise. We show example results in a variety of applications, including motion segmentation and long-term point tracking.

Original languageEnglish
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
Number of pages8
StatePublished - 2011

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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