Exploiting sparsity in dense optical flow

Xiaohui Shen*, Ying Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In this paper we validated that the dense optical flow field is sparse in certain frequency domains, while the flow gradient field is also sparse in image domain. Based on this sparsity prior, the optical flow estimation problem is casted as sparse signal recovery from highly shorted measurements. By minimizing its l1-norm in frequency domain and gradient domain, the model can accurately estimate the dense flow field without other assumptions. Outliers are further identified and removed in the flow denoising process to improve the results. Experiments show that our method significantly outperforms traditional methods based on global or piecewise smoothness priors. Moreover, it can well handle the complexity incurred by motion discontinuities.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages741-744
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Other

Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong
Period9/26/109/29/10

Keywords

  • Compressive sensing
  • L-norm minimization
  • Optical flow
  • Sparse representation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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