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Inlier Estimation for Moving Camera Motion Segmentation

  • Xuefeng LiangEmail author
  • Cuicui Zhang
  • Takashi Matsuyama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9006)

Abstract

In moving camera videos, motion segmentation is often performed on the optical flow. However, there exist two challenges: (1) Camera motions lead to three primary flows in optical flow: translation, rotation, and radial flow. They are not all solved in existing frameworks under Cartesian coordinate system; (2) A moving camera introduces 3D motion, the depth discontinuities cause the motion discontinuities that severely confuse the motion segmentation. Meanwhile, the mixture of the camera motion and moving objects’ motions make indistinctness between foreground and background. In this work, our solution is to find a low order polynomial to model the background flow field due to its coherence. To this end, we first amend the Helmholts-Hodge Decomposition by adding coherence constraints, which can handle translation, rotation, and radial flow fields under a unified framework. Secondly, we introduce an Incoherence Map and a progressive Quad-Tree partition to reject moving objects and motion discontinuities. Finally, the low order polynomial is achieved from the rest flow samples on two potentials in HHD. We present results on more than twenty videos from four benchmarks. Extensive experiments demonstrate a better performance in dealing with challenging scenes with complex backgrounds. Our method improves the segmentation accuracy of state-of-the-art by \(10\%\sim 30\%\).

Keywords

Optical Flow Camera Motion Radial Flow Motion Segmentation Depth Discontinuity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work is supported by: Japan Society for the Promotion of Science, Scientific Research KAKENHI for Grant-in-Aid for Young Scientists (ID:25730113).

Supplementary material

Supplementary material (avi 1,537 KB)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xuefeng Liang
    • 1
    Email author
  • Cuicui Zhang
    • 1
  • Takashi Matsuyama
    • 1
  1. 1.IST, Graduate School of InformaticsKyoto UniversityKyotoJapan

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