By Topic

Invariant action classification with volumetric data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Cuzzolin, F. ; Dipt. di Elettronica e Informazione, Politecnico di Milano, Milan, Italy ; Sarti, A. ; Tubaro, S.

We propose an action recognition algorithm in which the image sequences capturing a moving human body produced by a significant number of cameras are first used to generate a volumetric representation of the body by means of volumetric intersection. Classification is then performed directly on 3D data, making the system inherently insensitive to viewpoint dependence and motion trajectory variability. Suitable features are extracted from the voxset approximating the body, and fed to a hidden Markov model to produce a finite-state description of the motion. The Kullback-Leibler distance is finally used to classify new sequences.

Published in:

Multimedia Signal Processing, 2004 IEEE 6th Workshop on

Date of Conference:

29 Sept.-1 Oct. 2004