• DocumentCode
    3672167
  • Title

    Dense, accurate optical flow estimation with piecewise parametric model

  • Author

    Jiaolong Yang;Hongdong Li

  • Author_Institution
    Beijing Lab of Intelligent Information Technology, Beijing Institute of Technology, China
  • fYear
    2015
  • fDate
    6/1/2015 12:00:00 AM
  • Firstpage
    1019
  • Lastpage
    1027
  • 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.
  • Keywords
    "Parametric statistics","Estimation","Labeling","Benchmark testing","Motion segmentation","Adaptation models","Image segmentation"
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
  • Electronic_ISBN
    1063-6919
  • Type

    conf

  • DOI
    10.1109/CVPR.2015.7298704
  • Filename
    7298704