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


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
Number of pages4
StatePublished - 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong


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

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


Dive into the research topics of 'Exploiting sparsity in dense optical flow'. Together they form a unique fingerprint.

Cite this