Dense, accurate optical flow estimation with piecewise parametric model

Jiaolong Yang, Hongdong Li

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

    94 Citations (Scopus)

    Abstract

    This paper proposes a simple method for estimating dense and accurate optical flow field. It revitalizes an early idea of piecewise parametric flow model. A key innovation is that, we fit a flow field piecewise to a variety of parametric models, where the domain of each piece (i.e., each piece's shape, position and size) is determined adaptively, while at the same time maintaining a global inter-piece flow continuity constraint. We achieve this by a multi-model fitting scheme via energy minimization. Our energy takes into account both the piecewise constant model assumption and the flow field continuity constraint, enabling the proposed method to effectively handle both homogeneous motions and complex motions. The experiments on three public optical flow benchmarks (KITTI, MPI Sintel, and Middlebury) show the superiority of our method compared with the state of the art: it achieves top-tier performances on all the three benchmarks.

    Original languageEnglish
    Title of host publicationIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
    PublisherIEEE Computer Society
    Pages1019-1027
    Number of pages9
    ISBN (Electronic)9781467369640
    DOIs
    Publication statusPublished - 14 Oct 2015
    EventIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States
    Duration: 7 Jun 201512 Jun 2015

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Volume07-12-June-2015
    ISSN (Print)1063-6919

    Conference

    ConferenceIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
    Country/TerritoryUnited States
    CityBoston
    Period7/06/1512/06/15

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