A gesture expressive model based on Laban qualities

Arthur Truong, Hugo Boujut, Titus Zaharia

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

Abstract

Today, gesture analysis lacks of global models able to characterize motion expressivity and its communicational character. In this paper, we propose a set of new gesture descriptors inspired from Laban Movement Analysis (LMA) and based on 3D body trajectories. We test our descriptors ability to characterize human actions in a machine learning framework (with SVM and different random forest techniques). The results obtained on Microsoft Research Cambridge-12 (MSRC-12) dataset and show very high recognition rates (more than 97%).

Original languageEnglish
Title of host publicationProceedings 2014 IEEE 4th International Conference on Consumer Electronics - Berlin, ICCE-Berlin
EditorsFrancisco J. Bellido, Dietmar Hepper, Hans L. Cycon, Alexander Huhn
PublisherIEEE Computer Society
Pages168-172
Number of pages5
EditionFebruary
ISBN (Electronic)9781479961658
DOIs
Publication statusPublished - 5 Feb 2015
Externally publishedYes
Event2014 4th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin - Berlin, Germany
Duration: 7 Sept 201410 Sept 2014

Publication series

NameIEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
NumberFebruary
Volume2015-February
ISSN (Print)2166-6814
ISSN (Electronic)2166-6822

Conference

Conference2014 4th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Country/TerritoryGermany
CityBerlin
Period7/09/1410/09/14

Keywords

  • Gesture expressivity
  • Laban movement analysis
  • gesture recognition
  • machine learning
  • motion features

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