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Action Recognition in Intelligent Environments using Point Cloud Features Extracted from Silhouette Sequences (bibtex)
Action Recognition in Intelligent Environments using Point Cloud Features Extracted from Silhouette Sequences (bibtex)
by RB Rusu, J Bandouch, ZC Marton, N Blodow and M 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:
Action Recognition in Intelligent Environments using Point Cloud Features Extracted from Silhouette Sequences (RB Rusu, J Bandouch, ZC Marton, N Blodow and M Beetz), In IEEE 17th International Symposium on Robot and Human Interactive Communication (RO-MAN), Muenchen, Germany, 2008. 
Bibtex Entry:
@inproceedings{Rusu08ROMAN,
 author = {RB Rusu and J Bandouch and ZC Marton and N Blodow and M 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.
  },
}
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