Pfister, T, Charles, J and Zisserman, A (2014) Domain-adaptive discriminative one-shot learning of gestures. In: Fleet, D, Padjla, T, Schiele, B and Tuytelaars, T, (eds.) Computer Vision – ECCV 2014 13th European Conference, Proceedings, Part VI. 13th European Conference, 06-12 Sep 2014, Zurich, Switzerland. Springer International Publishing , 814 - 829. ISBN 9783319105987
Abstract
The objective of this paper is to recognize gestures in videos - both localizing the gesture and classifying it into one of multiple classes. We show that the performance of a gesture classifier learnt from a single (strongly supervised) training example can be boosted significantly using a 'reservoir' of weakly supervised gesture examples (and that the performance exceeds learning from the one-shot example or reservoir alone). The one-shot example and weakly supervised reservoir are from different 'domains' (different people, different videos, continuous or non-continuous gesturing, etc), and we propose a domain adaptation method for human pose and hand shape that enables gesture learning methods to generalise between them. We also show the benefits of using the recently introduced Global Alignment Kernel [12], instead of the standard Dynamic Time Warping that is generally used for time alignment. The domain adaptation and learning methods are evaluated on two large scale challenging gesture datasets: one for sign language, and the other for Italian hand gestures. In both cases performance exceeds the previous published results, including the best skeleton-classification-only entry in the 2013 ChaLearn challenge.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2014, Springer International Publishing. This is an author produced version of a paper published in Computer Vision – ECCV 2014 13th European Conference, Proceedings, Part VI. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10599-4_52 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Mar 2015 11:14 |
Last Modified: | 19 Dec 2022 13:30 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-10599-4_52 |
Status: | Published |
Publisher: | Springer International Publishing |
Identification Number: | 10.1007/978-3-319-10599-4_52 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:83897 |