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Tracking Humans Interacting with the Environment Using Efficient Hierarchical Sampling and Layered Observation Models (bibtex) [pdf]
@InProceedings{bandouch09hci,
  author =       {Jan Bandouch and Michael Beetz},
  title =        {Tracking Humans Interacting with the Environment Using Efficient Hierarchical Sampling and Layered Observation Models},
  booktitle =    {IEEE Int. Workshop on Human-Computer Interaction (HCI). In conjunction with ICCV2009},
  year =         {2009},
  bib2html_pubtype ={Conference Paper},
  bib2html_rescat  = {Perception},
  bib2html_groups = {Memoman},
  bib2html_funding = {CoTeSys},
  bib2html_domain  = {Assistive Household},
  abstract =     {  We present a markerless tracking system for unconstrained human
  motions which are typical for everyday manipulation tasks. Our
  system is capable of tracking a high-dimensional human model
  (51 DOF) without constricting the type of motion and the need for
  training sequences. The system reliably tracks humans that
  frequently interact with the environment, that manipulate objects,
  and that can be partially occluded by the environment.
  We describe and discuss two key components that substantially
  contribute to the accuracy and reliability of the system. First, a
  sophisticated hierarchical sampling strategy for recursive Bayesian
  estimation that combines partitioning with annealing strategies to enable
  efficient search in the presence of many local maxima. Second, a simple yet
  effective appearance model that allows for the combination of shape and
  appearance masks to implicitly deal with two cases of environmental occlusions
  by (1) subtracting dynamic non-human objects from the region of
  interest and (2) modeling objects (e.g. tables) that both occlude and
  can be occluded by human subjects.  The appearance model is based on
  bit representations that makes our algorithm well suited for
  implementation on highly parallel hardware such as commodity GPUs.
  Extensive evaluations on the HumanEva2 benchmarks show the potential
  of our method when compared to state-of-the-art Bayesian techniques.
  Besides the HumanEva2 benchmarks, we present results on more
  challenging sequences, including table setting tasks in a kitchen
  environment and persons getting into and out of a car mock-up.}
}
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Last edited 17.01.2013 13:54 by Quirin Lohr