German Conference on Pattern Recognition

Pattern Recognition pp 16-28 | Cite as

Discrete Optimization for Optical Flow

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

We propose to look at large-displacement optical flow from a discrete point of view. Motivated by the observation that sub-pixel accuracy is easily obtained given pixel-accurate optical flow, we conjecture that computing the integral part is the hardest piece of the problem. Consequently, we formulate optical flow estimation as a discrete inference problem in a conditional random field, followed by sub-pixel refinement. Naïve discretization of the 2D flow space, however, is intractable due to the resulting size of the label set. In this paper, we therefore investigate three different strategies, each able to reduce computation and memory demands by several orders of magnitude. Their combination allows us to estimate large-displacement optical flow both accurately and efficiently and demonstrates the potential of discrete optimization for optical flow. We obtain state-of-the-art performance on MPI Sintel and KITTI.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Moritz Menze
    • 1
  • Christian Heipke
    • 1
  • Andreas Geiger
    • 2
  1. 1.Leibniz Universität HannoverHanoverGermany
  2. 2.Max Planck Institute for Intelligent SystemsTübingenGermany

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