IS = { zkontrolovano 31 Jan 2011 },
  UPDATE  = { 2010-07-27 },
  author = {Grabner, Helmut and Matas, Ji{\v r}{\' i} and Van Gool, Luc
            and Cattin, Philippe},
  title = {Tracking the Invisible: Learning Where the Object Might be},
  booktitle =   {{CVPR} 2010: Proceedings of the 2010 IEEE Computer
            Society Conference on Computer Vision and Pattern Recognition},
  language = {english},
  year = {2010},
  pages = {1285--1292},
  month = {June},
  annote = { Objects are usually embedded into context. Visual context
    has been successfully used in object detection tasks, however, it
    is often ignored in object tracking. We propose a method to learn
    supporters which are, be it only temporally, useful for
    determining the position of the object of interest. Our approach
    exploits the General Hough Transform strategy. It couples the
    supporters with the target and naturally distinguishes between
    strongly and weakly coupled motions. By this, the position of an
    object can be estimated even when it is not seen directly (e.g.,
    fully occluded or outside of the image region) or when it changes
    its appearance quickly and significantly. Experiments show
    substantial improvements in model-free tracking as well as in the
    tracking of virtual points, e.g., in medical applications. },
  keywords =    {tracking},
  publisher =   {Omnipress},
  address =     {Madison, USA},
  book_pages =  {3523},
  prestige =    {important},
  day = {13--18},
  isbn = {978-1-4244-6984-0},
  issn = {1063-6919},
  venue = {San Francisco, USA},
  project = {FP7-ICT-247022 MASH},
  www = {http://cmp.felk.cvut.cz/~matas/papers/grabner-tracking_the_invisible-cvpr10.pdf},