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Real-Time Adaptive Hand Motion Recognition Using a Sparse Bayesian Classifier

  • Shu-Fai Wong
  • Roberto Cipolla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3766)

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Shu-Fai Wong
    • 1
  • Roberto Cipolla
    • 1
  1. 1.Department of EngineeringThe University of Cambridge 

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