@inproceedings{1f6636ff8d514281866cf7cf0ec147f8,
title = "High performance real-time gesture recognition using hidden markov models",
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.",
author = "Gerhard Rigoll and Andreas Kosmala and Stefan Eickeler",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1998.; International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction, 1997 ; Conference date: 17-09-1997 Through 19-09-1997",
year = "1998",
doi = "10.1007/BFb0052990",
language = "English",
isbn = "3540644245",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "69--80",
editor = "Martin Frohlich and Ipke Wachsmuth",
booktitle = "Gesture and Sign Language in Human Computer Interaction - International Gesture Workshop, Proceedings",
}