Recognition of human body posture from a cloud of 3D data points using wavelet transform coefficients

Werghi, N. and Xiao, Y. (2002) Recognition of human body posture from a cloud of 3D data points using wavelet transform coefficients. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, 20-21 May 2002, pp. 70-75. ISBN 9780769516028 (doi: 10.1109/AFGR.2002.1004135)

Abstract

Addresses the problem of recognizing a human body posture from a cloud of 3D points acquired by a human body scanner. Motivated by finding a representation that embodies a high discriminatory power between posture classes, a new type of feature is suggested, namely the wavelet transform coefficients (WTC) of the 3D data-point distribution projected on to the space of spherical harmonics. A feature selection technique is developed to find those features with high discriminatory power. Integrated within a Bayesian classification framework and compared with other standard features, the WTC showed great capability in discriminating between close postures. The qualities of the WTC features were also reflected in the experimental results carried out with artificially generated postures, where the WTC obtained the best classification rate

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Xiao, Mr Yijun
• Authors: Werghi, N., and Xiao, Y.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Institute of Electrical and Electronics Engineers
• ISBN: 9780769516028
• Copyright Holders: Copyright © 2002 Institute of Electrical and Electronics Engineers
• First Published: First published in Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher