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Segmentation of moving objects by long term video analysis

Peter Ochs, J. Malik, Thomas Brox
IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6): 1187 - 1200, Jun 2014
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Abstract: Motion is a strong cue for unsupervised object-level grouping. In this paper, we demonstrate that motion will be exploited most effectively, if it is regarded over larger time windows. Opposed to classical two-frame optical flow, point trajectories that span hundreds of frames are less susceptible to short term variations that hinder separating different objects. As a positive side effect, the resulting groupings are temporally consistent over a whole video shot, a property that requires tedious post-processing in the vast majority of existing approaches. We suggest working with a paradigm that starts with semi-dense motion cues first and that fills up textureless areas afterwards based on color. This paper also contributes the Freiburg-Berkeley motion segmentation (FBMS) dataset, a large, heterogeneous benchmark with 59 sequences and pixel-accurate ground truth annotation of moving objects.

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@Article{OB14b,
  author       = "P. Ochs and J. Malik and T. Brox",
  title        = "Segmentation of moving objects by long term video analysis",
  journal      = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
  number       = "6",
  volume       = "36",
  pages        = "1187 - 1200",
  month        = "Jun",
  year         = "2014",
  note         = "Preprint",
  url          = "http://lmb.informatik.uni-freiburg.de//Publications/2014/OB14b"
}

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