High performance real-time gesture recognition using hidden markov models

Gerhard Rigoll, Andreas Kosmala, Stefan Eickeler

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

52 Scopus citations

Abstract

An advanced real-time system for gesture recognition is presented, which is able to recognize complex dynamic gestures, such as “hand waving”, “spin”, “pointing”, and “head moving”. The recognition is based on global motion features, extracted from each difference image of the image sequence. The system uses Hidden Markov Models (HMMs) as statistical classifier. These HMMs are trained on a database of 24 isolated gestures, performed by 14 different people. With the use of global motion features, a recognition rate of 92.9% is achieved for a person and background independent recognition.

Original languageEnglish
Title of host publicationGesture and Sign Language in Human Computer Interaction - International Gesture Workshop, Proceedings
EditorsMartin Frohlich, Ipke Wachsmuth
PublisherSpringer Verlag
Pages69-80
Number of pages12
ISBN (Print)3540644245, 9783540644248
DOIs
StatePublished - 1998
Externally publishedYes
EventInternational Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction, 1997 - Bielefeld, Germany
Duration: 17 Sep 199719 Sep 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1371
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction, 1997
Country/TerritoryGermany
CityBielefeld
Period17/09/9719/09/97

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