@Article{Zimmermann-PAMI2009,
  IS = { zkontrolovano 19 Aug 2009 },
  UPDATE  = { 2009-03-23 },
  author =     {Zimmermann, Karel and Matas, Ji{\v r}{\'\i} and Svoboda, Tom{\'a}{\v s}},
  title =      {Tracking by an Optimal Sequence of Linear Predictors},
  year =       {2009},
  month =      {April},
  pages =      {677-692},
  journal =    {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  publisher =  {IEEE Computer Society},
  address =    {CA, USA},
  issn =       {0162-8828},
  volume =     {31},
  number =     {4},
  authorship = {40-30-30},
  annote = {We propose a learning approach to tracking explicitly
    minimizing the computational complexity of the tracking process
    subject to user-defined probability of failure (loss-of-lock) and
    precision.  The tracker is formed by a Number of Sequences of
    Learned Linear Predictors (NoSLLiP).  Robustness of NoSLLiP is
    achieved by modeling the object as a collection of local motion
    predictors --- object motion is estimated by the outlier-tolerant
    Ransac algorithm from local predictions. Efficiency of the NoSLLiP
    tracker stems from (i) the simplicity of the local predictors and
    (ii) from the fact that all design decisions - the number of local
    predictors used by the tracker, their computational complexity (ie
    the number of observations the prediction is based on), locations
    as well as the number of Ransac iterations are all subject to the
    optimization (learning) process. All time-consuming operations are
    performed during the learning stage - tracking is reduced to only
    a few hundreds integer multiplications in each step. On PC with
    1xK8 3200+, a predictor evaluation requires about 30 microseconds.
    The proposed approach is verified on publicly-available sequences
    with approximately 12000 frames with ground-truth. Experiments
    demonstrates, superiority in frame rates and robustness with
    respect to the SIFT detector, Lucas-Kanade tracker and other
    trackers.},
  keywords =   {Image processing and computer vision, 
                Scene analysis, Tracking},
  project =    {1ET101210407, FP6-IST-027787, 
                GACR 102/07/1317, 1M0567},
  psurl       = { [PDF] },
  www         = {http://cmp.felk.cvut.cz/demos/Tracking/linTrack/},
}