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Abstract for sfwong_bmvc05

Proc. British Machine Vision Conference (BMVC05), volume 1, pages 379-388, Oxford, UK


Shu-Fai Wong and Roberto Cipolla

Sept 2005

An approach to recognise 10 elementary gestures is proposed and it can be applied to sign language recognition. In this work, a motion gradient orientation image is extracted directly from a raw video input and transformed to a motion feature vector. This feature vector is then classified into one of the 10 elementary gestures by a sparse Bayesian classifier. A training set of 628 samples and a testing set of over 1000 samples have been obtained to evaluate the proposed method. A real-time system was built and trained with the training set. From the experiment, the reported classification accuracy is 90\% and the system can run in around 25 frames per second. Compared with other recently proposed methods that involve the use of hand tracking, the system can work reliably in real-time without relying on accurate tracking, and give a probabilistic output that is useful in complex motion analysis.

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© 2005 Cambridge University Engineering Dept
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