@InProceedings{Roth-MOTION2005,
  IS = { zkontrolovano 30 Nov 2005 },
  UPDATE  = { 2005-05-18 },
  author = {Roth, Daniel and Doubek, Petr and Van Gool, Luc},
  title = {Bayesian Pixel Classification for Human Tracking},
  booktitle = {7th IEEE Workshop on Applications of Computer
               Vision / IEEE Workshop on Motion and Video
               Computing (WACV/MOTION 2005)},
  year = {2005},
  volume = {2},
  pages = {78-83},
  editor = {Isaac Cohen},
  publisher = {IEEE Computer Society Press},
  address = {Los Alamitos, US},
  month = {January},
  day = {5-7},
  isbn = {0-7695-2271-8},
  book_pages = {276},
  venue = {Colorado, Breckenridge, USA},
  annote = {We present a monocular object tracker, able to detect and
    track multiple objects in non-controlled environments. Bayesian
    per-pixel classification is used to build a tracking framework
    that segments an image into foreground and background objects,
    based on expectations about object appearances and
    motions. Gaussian mixtures are used to build the color appearance
    models. The system adapts to changing lighting conditions, handles
    occlusions, and works in real-time. },
  keywords = {tracking, segmentation},
  authorship = { 60-30-10 },
  project = {Swiss SNF NCCR project IM2, ETH Z{\" u}rich project blue-c-II},
}