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BriefMatch: Dense binary feature matching for real-time optical flow estimation
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5698-5983
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7765-1747
2017 (English)In: Proceedings of the Scandinavian Conference on Image Analysis (SCIA17) / [ed] Puneet Sharma, Filippo Maria Bianchi, Springer, 2017, Vol. 10269, p. 221-233Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10269, p. 221-233
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords [en]
computer vision, optical flow, feature matching, real-time computation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-149418DOI: 10.1007/978-3-319-59126-1_19Scopus ID: 2-s2.0-85020383306ISBN: 978-3-319-59125-4 (print)OAI: oai:DiVA.org:liu-149418DiVA, id: diva2:1228880
Conference
Scandinavian Conference on Image Analysis (SCIA17), Tromsø, Norway, 12-4 June, 2017
Available from: 2018-06-28 Created: 2018-06-28 Last updated: 2018-08-24Bibliographically approved

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Eilertsen, GabrielForssén, Per-ErikUnger, Jonas

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