IS = { zkontrolovano 18 Jan 2009 },
  UPDATE  = { 2008-12-16 },
  author =      {Ellis, Liam and 
                 Matas, Ji{\v r}{\' \i} and
                 Bowden, Richard},
  title =       {Online Learning and Partitioning of Linear Displacement
                 Predictors for Tracking},
  authorship =  {50-30-20},
  year =        {2008},
  pages =       {33--42},
  booktitle =   {BMVC 2008: Proceedings of the 19th British
                 Machine Vision Conference},
  volume =      {1},
  editor =      {Everingham, M. and Needham, C.  and Fraille, R.},
  isbn =        {978-1-901725-36-0},
  book_pages =  {1194},
  publisher =   {BMVA},
  address =     {London, UK},
  month =       {September},
  day =         {1--4},
  venue =       {Leeds, UK},
  project =     {ICT-215078 DIPLECS},
  psurl =       {pdf},
  annote={ A novel approach to learning and tracking arbitrary image
    features is presented. Tracking is tackled by learning the mapping
    from image intensity differences to displacements. Linear
    regression is used, resulting in low computational cost. An
    appearance model of the target is built on-the-fly by clustering
    sub-sampled image templates. The medoidshift algorithm is used to
    cluster the templates thus identifying various modes or aspects of
    the target appearance, each mode is associated to the most
    suitable set of linear predictors allowing piecewise linear
    regression from image intensity differences to warp
    updates. Despite no hard-coding or offline learning, excellent
    results are shown on three publicly available video sequences and
    comparisons with related approaches made.},
  keywords =    {computer vision, tracking, offline learning },