Dynamic gesture recognition with Laban Movement Analysis and Hidden Markov Models

Abstract : In this paper, we propose a new approach for gesture recognition based upon the quantification of Laban Movement Analysis (LMA) concepts. The resulting body features are used to build a dictionary of key poses. Then, a soft assignment method is applied to the gesture sequences to obtain a gesture representation. The assignment results are used as input in a Hidden Markov Models (HMM) scheme for dynamic gesture recognition purposes. The proposed approach achieves high recognition rates (more than 92% for certain categories of gestures), when tested and evaluated on a corpus including 11 different actions. The high recognition rates obtained on two other datasets (Microsoft Gesture dataset [1] and UTKinect-Human Detection dataset [2]) show the relevance of our method
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01451527
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Wednesday, February 1, 2017 - 11:22:14 AM
Last modification on : Thursday, October 17, 2019 - 12:36:52 PM

Identifiers

Citation

Arthur Truong, Titus Zaharia. Dynamic gesture recognition with Laban Movement Analysis and Hidden Markov Models. CGI 2016 : 33rd Computer Graphics International , Jun 2016, Heraklion, Greece. pp.21 - 24, ⟨10.1145/2949035.2949041⟩. ⟨hal-01451527⟩

Share

Metrics

Record views

317