Clustering Method Evaluation for Hidden Markov Model Based Real-Time Gesture Recognition

Author

Prasad, Jay Shankar ; Nandi, G.C.

Author_Institution

Robot. & AI Lab., Indian Inst. of Inf. Technol., Allahabad, India

fYear

2009

fDate

27-28 Oct. 2009

Firstpage

419

Lastpage

423

Abstract

This paper deals with the development of high performance real-time system for complex dynamic gesture recognition. The various motion features are extracted from the video frames which are used by HMM classifier. We used several clustering techniques for performance evaluation of the classifier. Our system vectorises gestures into sequential symbols both for training and testing. We found very encouraging results and the proposed method has potential application in the field of human machine interaction.

Keywords

feature extraction; gesture recognition; hidden Markov models; human computer interaction; pattern classification; pattern clustering; real-time systems; video signal processing; classifier; clustering method; hidden Markov model; human machine interaction; motion feature extraction; real-time gesture recognition; video frames; Clustering methods; Data mining; Feature extraction; Hidden Markov models; Humans; Image recognition; Neural networks; Optical sensors; Principal component analysis; Real time systems; Clustering; Gesture; HMM;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on

Conference_Location

Kottayam, Kerala

Print_ISBN

978-1-4244-5104-3

Electronic_ISBN

978-0-7695-3845-7

Type

conf

DOI

10.1109/ARTCom.2009.99

Filename

5329365