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{rusu_action_2008,
 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},
 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.},
}

by RB Rusu, J Bandouch, ZC Marton, N Blodow and M Beetz

Abstract:

Reference:

Bibtex Entry:

@inproceedings{rusu_action_2008,
 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},
 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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