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Human Action Recognition using Global Point Feature Histograms and Action Shapes (bibtex)
  author = {Rusu, Radu Bogdan and Bandouch, Jan and Meier, Franziska and Essa,
	Irfan and Beetz, Michael},
  title = {Human Action Recognition using Global Point Feature Histograms and
	Action Shapes},
  journal = {Advanced Robotics journal, Robotics Society of Japan ({RSJ)}},
  year = {2009},
  abstract = {This article investigates the recognition of human actions from {3D}
	point clouds that encode the motions of people acting in sensor-distributed
	indoor environments. Data streams are time-sequences of silhouettes
	extracted from cameras in the environment. From the {2D} silhouette
	contours we generate space-time streams by continuously aligning
	and stacking the contours along the time axis as third spatial dimension.
	The space-time stream of an observation sequence is segmented into
	parts corresponding to subactions using a pattern matching technique
	based on suffix trees and interval scheduling. Then, the segmented
	space-time shapes are processed by treating the shapes as {3D} point
	clouds and estimating global point feature histograms for them. The
	resultant models are clustered using statistical analysis, and our
	experimental results indicate that the presented methods robustly
	derive different action classes. This holds despite large intra-class
	variance in the recorded datasets due to performances from different
	persons at different time intervals.}
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Last edited 29.01.2013 17:37 by Quirin Lohr