@inproceedings{ba77a12386be462ead6ffa6f8a01dafa,
title = "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 abignment method is applied to the gesture sequences to obtain a gesture representation. The abignment 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.",
keywords = "Body motion descriptors, Gesture recognition, Hidden Markov Model, Laban movement analysis, Soft abignment",
author = "Arthur Truong and Titus Zaharia",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 33rd Computer Graphics International Conference, CGI 2016 ; Conference date: 28-06-2016 Through 01-07-2016",
year = "2016",
month = jun,
day = "28",
doi = "10.1145/2949035.2949041",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "21--24",
booktitle = "Proceedings of the 33rd Computer Graphics International Conference, CGI 2016",
}