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Real-time hand gesture recognition for uncontrolled environments using adaptive SURF tracking and hidden conditional random fields

conference contribution
posted on 2013-01-01, 00:00 authored by Y Yao, Chang-Tsun LiChang-Tsun Li
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

8034

Issue

Part II

Series

Visual Computing Symposium

Pagination

542 - 551

Publisher

Springer

Location

Rethymnon, Crete, Greece

Place of publication

Berlin, Germany

Start date

2013-07-29

End date

2013-07-31

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783642419386

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2013, Springer-Verlag Berlin Heidelberg

Editor/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 Gotz

Title of proceedings

ISVC 2013 : Proceedings of the 9th International Symposium on Visual Computing