Search Contact information
University of Cambridge Home Department of Engineering
University of Cambridge > Engineering Department > Machine Intelligence Lab

Abstract for sfwong_iccv_hci05

Proc. IEEE International Workshop on Human-Computer Interaction (ICCV2005 Workshop on HCI), pages 170-179, Beijing, China

REAL-TIME ADAPTIVE HAND MOTION RECOGNITION USING A SPARSE BAYESIAN CLASSIFIER

Shu-Fai Wong and Roberto Cipolla

Oct 2005

An approach to increase adaptability of a recognition system, which can recognise 10 elementary gestures and be extended to sign language recognition, is proposed. In this work, recognition is done by firstly extracting a motion gradient orientation image from a raw video input and then classifying a feature vector generated from this image to one of the 10 gestures by a sparse Bayesian classifier. The classifier is designed in a way that it supports online incremental learning and it can be thus re-trained to increase its adaptability to an input captured under a new condition. Experiments show that the accuracy of the classifier can be boosted from less than 40\% to over 80\% by re-training it using 5 newly captured samples from each gesture class. Apart from having a better adaptability, the system can work reliably in real-time and give a probabilistic output that is useful in complex motion analysis.


(ftp:) sfwong_iccv_hci05.pdf (http:) sfwong_iccv_hci05.pdf

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.

© 2005 Cambridge University Engineering Dept
Information provided by milab-maintainer