@InProceedings{Cerman-SCIA09,
  IS = { zkontrolovano 28 Jan 2010 },
  UPDATE  = { 2009-07-27 },
  booktitle = { SCIA 2009: Proceedings of the 16th Scandinavian Conference on Image Analysis },
  editor    = { Salberg, Arnt-B{\o e}rre and Hardeberg, Jon Yngve and Jenssen, Robert  },
  publisher = { Springer-Verlag },
  address   = { Berlin, Germany },
  isbn      = { 978-3-642-02229-6 },
  book_pages = { 783 },
  title     = { Sputnik Tracker: Looking for a Companion Improves Robustness of the Tracker },
  author    = { Cerman, Luk{\'a}{\v s} and Hlav{\'a}{\v c}, V{\'a}clav  and Matas, Ji{\v r}{\'\i} },
  pages     = { 291--300 },
  year      = { 2009 },
  month     = { June },
  day       = { 15--18 },
  venue     = { Oslo, Norway },
  project   = { 1M0567, ICT-215078 DIPLECS },
  keywords  = { tracking, segmentation },
  annote = { Tracked objects rarely move alone. They are often
    temporarily accompanied by other objects undergoing similar
    motion. We propose a novel tracking algorithm called Sputnik
    (Sputnik, pronounced sput-nik in Russian, was the first
    Earth-orbiting satellite launched in 1957. According to
    Merriam-Webster dictionary, the English translation of the Russian
    word sputnik is a travelling companion.) Tracker. It is capable of
    identifying which image regions move coherently with the tracked
    object. This information is used to stabilize tracking in the
    presence of occlusions or fluctuations in the appearance of the
    tracked object, without the need to model its dynamics.  In
    addition, Sputnik Tracker is based on a novel template tracker
    integrating foreground and background appearance cues.  The time
    varying shape of the target is also estimated in each video frame,
    together with the target position. The time varying shape is used
    as another cue when estimating the target position in the next
    frame.},
  series      = { Lecture Notes in Computer Science },
  number      = { 5575 },
}