Algorithmic Impromovement Of Dynamic Hand Gesture Recognition Based On HMM Algorithm

DOI

10.2991/mce-14.2014.43How to use a DOI?

Keywords

Dynamic gesture recognition; HMM algorithm; threshold method; Forward—Backward algorithm; recognition rate

Abstract

Compared with other methods of dynamic gesture recognition, dynamic hand gesture recognition based on Hidden Markov Model (HMM) is more widely used. This method aims at analyzing hand signals. In traditional HMM method, any gestures would be calculated with the largest probability as the final recognition result according to Forward – Backward algorithm [1]. In this paper, We study and propose improved algorithm by using some methods like graphics normalizing, probability range limit, number of points limit, coding species limit, etc. to enhance the HMM method for improving the recognition rate and reducing the false detection rate. Experimental results show that the gesture trajectory recognition rate of such method is about 94%, which is higher than the traditional rate of 80%. Furthermore, the excluding error recognition rate, which was not dealt in the past, has reached 100%. Besides, our algorithm can be widely used because of its simple method of equipment, high precision and better robustness.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xue Xue
AU  - Zhuojia Li
AU  - Chuyu Hong
PY  - 2014/03
DA  - 2014/03
TI  - Algorithmic Impromovement Of Dynamic Hand Gesture Recognition Based On HMM Algorithm
BT  - Proceedings of the 2014 International Conference on Mechatronics, Control and Electronic Engineering
PB  - Atlantis Press
SP  - 198
EP  - 201
SN  - 1951-6851
UR  - https://doi.org/10.2991/mce-14.2014.43
DO  - 10.2991/mce-14.2014.43
ID  - Xue2014/03
ER  -