Object segmentation by long term analysis of point trajectories

Thomas Brox, J. Malik
European Conference on Computer Vision (ECCV), Springer, LNCS, Sept. 2010
Abstract: Unsupervised learning requires a grouping step that defines which data belong together. A natural way of grouping in images is the segmentation of objects or parts of objects. While pure bottom-up segmentation from static cues is well known to be ambiguous at the object level, the story changes as soon as objects move. In this paper, we present a method that uses long term point trajectories based on dense optical flow. Defining pair-wise distances between these trajectories allows to cluster them, which results in temporally consistent segmentations of moving objects in a video shot. In contrast to multi-body factorization, points and even whole objects may appear or disappear during the shot. We provide a benchmark dataset and an evaluation method for this so far uncovered setting.

Other associated files : brox_eccv10.pdf [1MB]  

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BibTex reference

  author       = "T.Brox and J.Malik",
  title        = "Object segmentation by long term analysis of point trajectories",
  booktitle    = "European Conference on Computer Vision (ECCV)",
  series       = "Lecture Notes in Computer Science",
  month        = "Sept.",
  year         = "2010",
  publisher    = "Springer",
  url          = "http://lmb.informatik.uni-freiburg.de/Publications/2010/Bro10c"

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