By Topic

Spotting dynamic hand gestures in video image sequences using hidden Markov models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Morguet, P. ; Inst. for Human-Machine-Commun., Munich Univ. of Technol., Germany ; Lang, M.

A new and general stochastic approach to find and identify dynamic gestures in continuous video streams is presented. Hidden Markov models (HMMs) are used to solve this combined problem of temporal segmentation and classification in an integral way. Basically, an improved normalized Viterbi algorithm allows one to continuously observe the output scores of the HMMs at every time step. Characteristic peaks in the output scores of the respective models indicate the presence of gestures. Our experiments in the domain of hand gesture spotting provided excellent recognition results and very low temporal detection delays

Published in:

Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on

Date of Conference:

4-7 Oct 1998