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BriefMatch: Dense binary feature matching for real-time optical flow estimation

Gabriel Eilertsen, Per-Erik Forssén, Jonas Unger
SCIA17, Tromsø, Norway
Proceedings of the Scandinavian Conference on Image Analysis (SCIA17)
June 2017


Research in optical flow estimation has to a large extent focused on achieving the best possible quality with no regards to running time. Nevertheless, in a number of important applications the speed is crucial. To address this problem we present BriefMatch, a real-time optical flow method that is suitable for live applications. The method combines binary features with the search strategy from PatchMatch in order to efficiently find a dense correspondence field between images. We show that the BRIEF descriptor provides better candidates (less outlier-prone) in shorter time, when compared to direct pixel comparisons and the Census transform. This allows us to achieve high quality results from a simple filtering of the initially matched candidates. Currently, BriefMatch has the fastest running time on the Middlebury benchmark, while placing highest of all the methods that run in shorter than 0.5 seconds.

Full Paper

Portable document format file PDF ()
Proceedings from SCIA 2017 are accessible in Springer Link
GIT repository for the source code.

Bibtex entry

  author = 	 {Gabriel Eilertsen and Per-Erik Forss\'en and Jonas Unger},
  title = 	 {BriefMatch: Dense binary feature matching for real-time optical flow estimation},
  booktitle = {Proceedings of the Scandinavian Conference on Image Analysis ({SCIA17})},
  year = 	 {2017},
  month = 	 {June},
  address = 	 {Troms\o{}, Norway},
  publisher = {Springer International Publishing},
  note =         {VR Project: Learnable Camera Motion Models, 2014-5928}

Per-Erik Forssén

Per-Erik Forssén


Computer Vision Laboratory
Department of Electrical Engineering
Building B
Room 2D:521
SE-581 83 Linköping, Sweden
+46(0)13 285654

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