Inferring stochastic regular grammar with nearness information for human action recognition

Kyungeun Cho, Hyungje Cho, Kyhyun Um

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

2 Scopus citations

Abstract

In this paper, we present an extended scheme of human action recognition with nearness information between hands and other body parts for the purpose of automatically analyzing nonverbal actions of human beings. First, based on the principle that a human action can be defined as a combination of multiple articulation movements, we apply the inference of stochastic grammars. We measure and quantize each human action in 3D coordinates and make two sets of 4-chain-code for xy and zy projection planes, so that they are appropriate for the stochastic grammar inference method. Next, we extend the stochastic grammar inferring method by applying nearness information. We confirm that various physical actions are correctly classified against a set of real-world 3D temporal data with this method in experiments. Our experiments show that this extended method reveals comparatively successful achievement with a 92.7% recognition rate of 60 movements of the upper body.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - Third International Conference, ICIAR 2006, Proceedings
PublisherSpringer Verlag
Pages193-204
Number of pages12
ISBN (Print)3540448942, 9783540448945
DOIs
StatePublished - 2006
Event3rd International Conference on Image Analysis and Recognition, ICIAR 2006 - Povoa de Varzim, Portugal
Duration: 18 Sep 200620 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4142 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Image Analysis and Recognition, ICIAR 2006
Country/TerritoryPortugal
CityPovoa de Varzim
Period18/09/0620/09/06

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