IS = { zkontrolovano 01 Feb 2010 },
  UPDATE  = { 2010-01-08 },
  author = {Kalal, Zdenek and Matas, Jiri and Mikolajczyk, Krystian},
  title = {Online learning of robust object detectors 
           during unstable tracking},
  year = {2009},
  annote = {This work investigates the problem of robust, longterm
    visual tracking of unknown objects in unconstrained
    environments. It therefore must cope with frame-cuts, fast camera
    movements and partial/total object occlusions/ disappearances. We
    propose a new approach, called Tracking-Modeling-Detection (TMD)
    that closely integrates adaptive tracking with online learning of
    the object-specific detector. Starting from a single click in the
    first frame, TMD tracks the selected object by an adaptive
    tracker. The trajectory is observed by two processes (growing and
    pruning event) that robustly model the appearance and build an
    object detector on the fly. Both events make errors, the stability
    of the system is achieved by their cancellation. The learnt
    detector enables re-initialization of the tracker whenever
    previously observed appearance reoccurs. We show the real-time
    learning and classification is achievable with random forests. The
    performance and the long-term stability of TMD is demonstrated and
    evaluated on a set of challenging video sequences with various
    objects such as cars, people and animals.},
  keywords = {tracking, online learning, object detection},
  booktitle = {3rd On-line learning for Computer Vision Workshop OLCV'09
               (held in conjunction with ICCV 2009)},
  pages =      {1417--1424},
  publisher =  {IEEE Computer Society},
  address   =  {Piscataway, USA},
  isbn =       {978-1-4244-4441-0},
  book_pages = {2235},
  month =      {October},
  day =        {3},
  venue =      {Kyoto, Japan},
  authorship   = {34-33-33},
  organization = {IEEE Computer Society},
  prestige =   {international},
  project = {ICT-215078 DIPLECS},
  note = {CD-ROM},