IS = { zkontrolovano 25 Jan 2014 },
  UPDATE  = { 2014-01-06 },
  author =      {Lebeda, Karel and Hadfield, Simon and 
                 Bowden, Richard  and Matas, Ji{\v r}{\' i}},
  title =       {Long-Term Tracking Through Failure Cases},
  year =        {2013},
  pages =       {153-160},
  booktitle =   {2013 IEEE International Conference on Computer Vision (ICCV 2013) Worskhops},
  publisher =   {IEEE},
  address =     {Piscataway, US},
  isbn =        {978-0-7695-5161-6},
  issn =        {1550-5499},
  book_pages =  {915},
  month =      {December},
  day =        {2},
  venue =      {Sydney, Australia},
  annote =      {Long term tracking of an object, given only a single instance in an initial
    frame, remains an open problem. We propose a visual tracking algorithm,
    robust to many of the difficulties which often occur in real-world scenes.
    Correspondences of edge-based features are used, to overcome the reliance on
    the texture of the tracked object and improve invariance to lighting.
    Furthermore we address long-term stability, enabling the tracker to recover
    from drift and to provide redetection following object disappearance or
    occlusion. The two-module principle is similar to the successful
    state-of-the-art long-term TLD tracker, however our approach extends to
    cases of low-textured objects. Besides reporting our results on the VOT
    Challenge dataset, we perform two additional experiments. Firstly, results
    on short-term sequences show the performance of tracking challenging objects
    which represent failure cases for competing state-of-the-art approaches.
    Secondly, long sequences are tracked, including one of almost 30 000 frames
    which to our knowledge is the longest tracking sequence reported to date.
    This tests the re-detection and drift resistance properties of the tracker.
    All the results are comparable to the state-of-the-art on sequences with
    textured objects and superior on non-textured objects. The new annotated
    sequences are made publicly available.},
 keywords =    {tracking, featureless objects, edge-based},
  prestige =   {international},
  authorship =  {25-25-25-25},
  project =     {GACR P103/12/G084, EPSRC EP/I011811/1},
  doi =         {10.1109/ICCVW.2013.26},