@InProceedings{Zimmermann-NRTL-2007,
  IS = { zkontrolovano 15 Dec 2007 },
  UPDATE  = { 2007-10-08 },
 author =       {Zimmermann, Karel and Svoboda, Tom{\' a}{\v s} and 
                 Matas, Ji{\v r}{\' i}},
 title =        {Adaptive parameter optimization for real-time tracking},
 booktitle =    {Proceedings of 11th IEEE International Conference on 
                 Computer Vision, workshop on Non-rigid registration and 
                 tracking through learning},
 year =         {2007},
 venue =        {Rio de Janeiro, Brazil},
 month =        {October},
 day =          {14-20},
 project =      {1ET101210407, FP6-IST-027787, GACR 102/07/1317},
 psurl =        {[zimmerk-nrtl07.pdf]},
 authorship =   {34-33-33},
 keywords =     {tracking, real-time, motion estimation},
 annote = {Adaptation of a tracking procedure combined in a common way
   with a Kalman filter is formulated as an constrained optimization
   problem, where a trade-off between precision and loss-of-lock
   probability is explicitly taken into account. While the tracker is
   learned in order to minimize computational complexity during a
   learning stage, in a tracking stage the precision is maximized
   online under a constraint imposed by the loss-of-lock probability
   resulting in an optimal setting of the tracking procedure. We
   experimentally show that the proposed method converges to a steady
   solution in all variables.  In contrast to a common Kalman filter
   based tracking, we achieve a significantly lower state covariance
   matrix.  We also show, that if the covariance matrix is
   continuously updated, the method is able to adapt to a different
   situations. If a dynamic model is precise enough the tracker is
   allowed to spend a longer time with a fine motion estimation,
   however, if the motion gets saccadic, i.e.  unpredictable by the
   dynamic model, the method automatically gives up the precision in
   order to avoid loss-of-lock.},
isbn        = { 978-1-4244-1631-8 },
issn        = { 1550-5499 },
publisher   = { Omnipress },
address     = { Madison, USA },
pages       = { 8 },
book_pages  = { 96 },
}