@MastersThesis{Klinger-TR-2012-01,
  IS = { zkontrolovano 14 Jan 2014 },
  UPDATE  = { 2012-03-07 },
  author = {Klinger, Miloslav},
  supervisor = {Hlav{\'a}{\v c}, V{\'a}clav},
  title = {Automatic classification of the {Parkinson's} patient
                  stiffness from a single videosequence},
  school = {Center for Machine Perception, 
            K13133 FEE Czech Technical University},
  address = {Prague, Czech Republic},
  year = {2012},
  month = {January},
  day = {23},
  type = {{MSc Thesis CTU--CMP--2012--01}},
  issn = {1213-2365},
  pages = {38},
  figures = {11},
  authorship = {100},
  psurl = {[Klinger-TR-2012-01.pdf]},
  project = {FP7-ICT-247525 HUMAVIPS, FP7-ICT-247870 NIFTi},
  annote = {This work presents a system for detection and tracking
    humans (Parkinson disease patients in our special case) in their
    natural home environment observed by a static single color
    surveillance camera. The purposed method emloys modified TLD
    tracker. The modifications enable the TLD to track multiple
    objects of multiple different classes at a time. The TLD can newly
    benefit from an external object detector. The detector
    (Felzenszwalb's detector in our case) allows the system to
    automatically initialize the tracking procedure, it helps the
    tracker to keep focused on the object and to terminate the
    tracking correctly. Resulting tracklets are automatically sorted
    out between individual persons and those belonging to the patient
    are used for assessing patient's stiffnes. The method was
    implemented and tested on an extensive (8 hours) videosequence.},
  keywords = {Parkinson's disease, human, tracking, detection, 
              health state, videosequence},
}