Gesture recognition system based on Adaptive Resonance Theory

Author

Park, Paul K. J. ; Jun Haeng Lee ; Chang Woo Shin ; Hyun-Surk Ryu ; Byung-Chang Kang ; Carpenter, G.A. ; Grossberg, Stephen

Author_Institution

Frontier IT Lab., Samsung Adv. Inst. of Technol., Yongin, South Korea

fYear

2012

fDate

11-15 Nov. 2012

Firstpage

3818

Lastpage

3822

Abstract

We report on the moving hand gesture recognition technique using Adaptive Resonance Theory (ART). To detect the start and end points of a continuous moving gesture (known as “gesture spotting” problem), we propose the adaptive distributed prediction technique. Our results show that, unlike conventional non-recurrent neural networks, the proposed technique can be utilized usefully in reliable real-time learning (2000 times faster than with alternative methods) and recognition of continuously moving patterns.

Keywords

adaptive resonance theory; gesture recognition; learning (artificial intelligence); prediction theory; ART; adaptive distributed prediction technique; adaptive resonance theory; continuous moving gesture end point detection; continuous moving gesture start point detection; continuously moving patterns; gesture spotting problem; moving hand gesture recognition technique; nonrecurrent neural networks; reliable real-time learning; Adaptive systems; Feature extraction; Gesture recognition; Humans; Subspace constraints; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition (ICPR), 2012 21st International Conference on

Conference_Location

Tsukuba

ISSN

1051-4651

Print_ISBN

978-1-4673-2216-4

Type

conf

Filename

6460997