A gesture expressive model based on Laban qualities

Author

Truong, Arthur ; Boujut, Hugo ; Zaharia, Titus

Author_Institution

ARTEMIS Dept., Inst. Mines-Telecom, Evry, France

fYear

2014

fDate

7-10 Sept. 2014

Firstpage

168

Lastpage

172

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%).

Keywords

gesture recognition; image motion analysis; learning (artificial intelligence); support vector machines; 3D body trajectories; LMA; Laban Movement Analysis; Laban qualities; Microsoft Research Cambridge-12 dataset; SVM; gesture descriptors; gesture expressive model; human action characterization; machine learning framework; random forest techniques; recognition rates; Analytical models; Conferences; Gesture recognition; Hidden Markov models; Joints; Shape; Trajectory; Gesture expressivity; Laban movement analysis; gesture recognition; machine learning; motion features;

fLanguage

English

Publisher

ieee

Conference_Titel

Consumer Electronics ??? Berlin (ICCE-Berlin), 2014 IEEE Fourth International Conference on

Conference_Location

Berlin

Type

conf

DOI

10.1109/ICCE-Berlin.2014.7034309

Filename

7034309