@MastersThesis{Prikner-TR-2008-11,
  UPDATE  = { 2009-01-27 },
  author =       {Prikner, Rostislav},
  supervisor =   {Svoboda, Tom{\'a}{\v s}},
  title =        {Pictorial Structural Models for Human Detection in
                  Videos},
  school =       {Center for Machine Perception, K13133 FEE Czech
                  Technical University},
  address =      {Prague, Czech Republic},
  year =         {2008},
  month =        {June},
  day =          {19},
  type =         {{MSc Thesis CTU--CMP--2008--11}},
  issn =         {1213-2365},
  pages =        {71},
  psurl =        {[Prikner-TR-2008-11.pdf]},
  project =      {1ET101210407},
  annote = {This paper describes the detection of a human body in
    images. Two different approaches are used. First approach detects
    a human body by using a single detection window based on features
    of the image gradients (HOGs) and it uses a cascade of classifiers
    to speed up the computing time. Second approach is based on
    matching of pictorial structures. An articulate model of the human
    body is assembled from individual parts (head, torso, limbs,
    etc). A human body model is represented as a collection of the
    parts arranged in a deformable configuration. Single body parts
    are detected by using color characteristics, that are gained from
    training examples. We advance the standard implementation by
    detecting shapes of multiple scales. The method is accelerated by
    vertical symmetry of a human body. The windowed human detector is
    applied in order to reduce the state space. We propose a method
    for unsupervised learning of color appearance of the human body
    parts. This approach makes the detection (using matching of the
    pictorial structures) more robust. The method integrates the fast
    human detector, the pictorial structures matching and image
    segmentation based on graph cuts. All the used methods are tested
    on real datasets. },
  keywords =     {pictorial structures, motion capture, tracking,
                  human detection},
}