Human Action Recognition by Inference of Stochastic Regular Grammars

Kyungeun Cho, Hyungje Cho, Kyhyun Um

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

10 Scopus citations

Abstract

In this paper, we present a new method of recognizing human actions by inference of stochastic grammars for the purpose of automatic analysis of nonverbal actions of human beings. We applied the principle that a human action can be defined as a combination of multiple articulation movements. We measure and quantize each articulation movements in 3D and represent two sets of 4-connected chain code for xy and zy projection planes, so that they are appropriate for the stochastic grammar inference method. This recognition method is tested by using 900 actions of human upper body. The result shows a comparatively successful achievement of 93.8% recognition rate through the experiments of 8 action types of head and 84.9% recognition rate of 60 action types of upper body.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAna Fred, Terry Caelli, Robert P.W. Duin, Dick de Ridder, Aurelio Campilho
PublisherSpringer Verlag
Pages388-396
Number of pages9
ISBN (Print)9783540225706
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3138
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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