Action Recognition in Intelligent Environments using Point Cloud Features Extracted from Silhouette Sequences (bibtex)
by Radu Bogdan Rusu, Jan Bandouch, Zoltan Csaba Marton, Nico Blodow, Michael Beetz
Abstract:
In this paper we present our work on human action recognition in intelligent environments. We classify actions by looking at a time-sequence of silhouettes extracted from various camera images. By treating time as the third spatial dimension we generate so-called space-time shapes that contain rich information about the actions. We propose a novel approach for recognizing actions, by representing the shapes as 3D point clouds and estimating feature histograms for them. Preliminary results show that our method robustly derives different classes of actions, even in the presence of large variability in the data, coming from different persons at different time intervals.
Reference:
Radu Bogdan Rusu, Jan Bandouch, Zoltan Csaba Marton, Nico Blodow, Michael Beetz, "Action Recognition in Intelligent Environments using Point Cloud Features Extracted from Silhouette Sequences", In IEEE 17th International Symposium on Robot and Human Interactive Communication (RO-MAN), Muenchen, Germany, 2008.
Bibtex Entry:
@InProceedings{Rusu08ROMAN,
  author    = {Radu Bogdan Rusu and Jan Bandouch and Zoltan Csaba Marton and Nico Blodow and Michael Beetz},
  title     = {{Action Recognition in Intelligent Environments using Point Cloud Features Extracted from Silhouette Sequences}},
  booktitle = {IEEE 17th International Symposium on Robot and Human Interactive Communication (RO-MAN), Muenchen, Germany},
  year      = {2008},
  bib2html_pubtype = {Conference Paper},
  bib2html_rescat  = {Perception},
  bib2html_groups  = {Memoman, EnvMod},
  bib2html_funding = {CoTeSys},
  bib2html_domain  = {Assistive Household},
  abstract  = {
               In this paper we present our work on human action recognition in intelligent
               environments. We classify actions by looking at a time-sequence of
               silhouettes extracted from various camera images. By treating time as the
               third spatial dimension we generate so-called space-time shapes that contain
               rich information about the actions. We propose a novel approach for
               recognizing actions, by representing the shapes as 3D point clouds and
               estimating feature histograms for them. Preliminary results show that our
               method robustly derives different classes of actions, even in the presence
               of large variability in the data, coming from different persons at different
               time intervals.
  }
}
Powered by bibtexbrowser