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Real-time hand gesture recognition for uncontrolled environments using adaptive SURF tracking and hidden conditional random fields
Challenges from the uncontrolled environments are the main difficulties in making hand gesture recognition methods robust in real-world scenarios. In this paper, we propose a real-time and purely vision-based method for hand gesture recognition in uncontrolled environments. A novel tracking method is introduced to track multiple hand candidates from the first frame. The movement directions of all hand candidates are extracted as trajectory features. A modified HCRF model is used to classify gestures. The proposed method can survive challenges including: gesturing hand out of the scene, pause during gestures, complex background, skin-coloured regions moving in background, performers wearing short sleeve and face overlapping with hand. The method has been tested on Palm Graffiti Digits database and Warwick Hand Gesture database. Experimental results show that the proposed method can perform well in uncontrolled environments.
History
Event
Visual Computing. Symposium (9th : 2013 : Rethymnon, Crete, Greece)Volume
8034Issue
Part IISeries
Visual Computing SymposiumPagination
542 - 551Publisher
SpringerLocation
Rethymnon, Crete, GreecePlace of publication
Berlin, GermanyPublisher DOI
Start date
2013-07-29End date
2013-07-31ISSN
0302-9743eISSN
1611-3349ISBN-13
9783642419386Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2013, Springer-Verlag Berlin HeidelbergEditor/Contributor(s)
G Bebis, R Boyle, B Parvin, D Koracin, B Li, F Porikli, V Zordan, J Klosowski, S Coquillart, X Luo, M Chen, D GotzTitle of proceedings
ISVC 2013 : Proceedings of the 9th International Symposium on Visual ComputingUsage metrics
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