@conference {cCanton-Ferrer06a,
	title = {Human Model and Motion Based 3D Action Recognition in Multiple View Scenarios},
	booktitle = {14th European Signal Processing Conference},
	year = {2006},
	pages = {1{\textendash}1},
	abstract = {<p>This paper presents a novel view-independent approach to the recognition of human gestures of several people in low resolution sequences from multiple calibrated cameras. In contraposition with other multi-ocular gesture recognition systems based on generating a classification on a fusion of features coming from different views, our system performs a data fusion (3D representation of the scene) and then a feature extraction and classification. Motion descriptors introduced by Bobick et al. for 2D data are extended to 3D and a set of features based on 3D invariant statistical moments are computed. A simple ellipsoid body model is fit to incoming 3D data to capture in which body part the gesture occurs thus increasing the recognition ratio of the overall system and generating a more informative classification output. Finally, a Bayesian classifier is employed to perform recognition over as mall set of actions. Results are provided showing the effectiveness of the proposed algorithm in a SmartRoom scenario.</p>},
	author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s}
}
