@Article{ellis-IJCV2011,
  IS = { zkontrolovano 08 Sep 2011 },
  UPDATE  = { 2011-01-14 },
  author = {Ellis, Liam  and 
            Dowson, Nicolas and
            Matas, Ji{\v r}{\'\i}  and
            Bowden, Richard },
  title =      {Linear Regression and Adaptive Appearance Models
                for Fast Simultaneous Modelling and Tracking},
  year =       {2011},
  month =      {accepted for publication on: 7 June 2010 },
  pages =      {154--179},
  journal =    {International Journal of Computer Vision},
  publisher =  {Springer},
  address =    {New York, USA},
  issn =       {0920-5691},
  volume =     {95},
  number =     {2},
  month =      {October},
  authorship = {25-25-25-25},
  annote = { This work proposes an approach to tracking by regression
   that uses no hard-coded models and no offline learning stage. The
   Linear Predictor (LP) tracker has been shown to be highly
   computationally efficient, resulting in fast tracking. Regression
   tracking techniques tend to require offline learning to learn
   suitable regression functions. This work removes the need for
   offline learning and therefore increases the applicability of the
   technique. The online-LP tracker can simply be seeded with an
   initial target location, akin to the ubiquitous Lucas-Kanade
   algorithm that tracks by registering an image template via
   minimisation.  A fundamental issue for all trackers is the
   representation of the target appearance and how this representation
   is able to adapt to changes in target appearance over time. The two
   proposed methods, LP-SMAT and LP-MED, demonstratthe ability to
   adapt to large appearance variations by incrementally building an
   appearance model that identifies modes or aspects of the target
   appearance and associates these aspects to the Linear Predictor
   trackers to which they are best suited. Experiments comparing and
   evaluating regression and registration techniques are presented
   along with performance evaluations favourably comparing the
   proposed tracker and appearance model learning methods to other
   state of the art simultaneous modelling and tracking approaches.  },
  keywords =  { Regression tracking, Online appearance modelling },
  project =   { ICT-215078 DIPLECS only EU, GACR 102/07/1317 },
  psurl =     { DOI 10.1007/s11263-010-0364-4},
}