IS = { zkontrolovano 18 Jan 2009 },
  UPDATE  = { 2008-12-23 },
  author =      {Grabner, Helmut and {\v S}ochman, Jan and 
                 Bischof, Horst and Matas, Ji{\v r}{\'i }},
  title =       {Training Sequential On-line Boosting Classifier for Visual Tracking},
  year =        {2008},
  pages =       {4},
  booktitle =   {ICPR 2008: Proceedings of the 19th International 
                 Conference on Pattern Recognition},
  isbn =        {978-1-4244-2174-9},
  issn =        {1051-4651},
  editor =      {Borgefors, G. and Flynn, P.},
  publisher =   {Omnipress},
  address =     {2600 Anderson Street, Madison, USA},
  book_pages =  {1542},
  month =       {December},
  day =         {7-11},
  venue =       {Tampa, USA},
  organization ={IAPR},
  annote = {On-line boosting allows to adapt a trained classifier to
    changing environmental conditions or to use sequentially available
    training data. Yet, two important problems in the on-line boosting
    training remain unsolved: (i) classifier evaluation speed
    optimization and, (ii) automatic classifier complexity
    estimation. In this paper we show how the on-line boosting can be
    combined with Wald's sequential decision theory to solve both of
    the problems.The properties of the proposed on-lineWaldBoost
    algorithm are demonstrated on a visual tracking problem.  The
    complexity of the classifier is changing dynamically depending on
    the difficulty of the problem. On average, a speedup of a factor
    of 5-10 is achieved compared to the non-sequential on-line
  keywords =    {on-line boosting, WaldBoost, tracking, sequential},
  prestige =    {international},
  authorship =  {25-25-25-25},
  note =        {CD-ROM},
  project =     {FP6-IST-027113, GACR 102/07/1317},
psurl    =    {[pdf]},