In this paper we propose a reliable gesture recognition system that could be run on low-level machines in real-time, which is practical in human-robot interaction scenarios. The system is based on a Random Forest classifier fed with Motion History Images(MHI) as classi?cation features. To detect fast continuous gestures as well as to improve the robustness, we introduce a feedback mechanism for parameter tuning. We applied the system as a component in the child-robot imitation game of ALIZ-E project.
Wang, W, Enescu, V & Sahli, H 2012, Fast Learning-based Gesture Recognition for Child-robot Interactions. in H Cuayáhuitl, L Frommberger, N Dethlefs & H Sahli (eds), Proceedings of ECAI’12 workshop on Machine Learning for Interactive Systems: Bridging the Gap between Language, Motor Control and Vision. vol. 1, Proceedings of ECAI’12 workshop on Machine Learning for Interactive Systems: Bridging the Gap between Language, Motor Control and Vision, no. 1, pp. 13-15, ECAI’12 workshop on Machine Learning for Interactive Systems: Bridging the Gap between Language, Motor Control and Vision, Montpellier, France, 27/08/12.
Wang, W., Enescu, V., & Sahli, H. (2012). Fast Learning-based Gesture Recognition for Child-robot Interactions. In H. Cuayáhuitl, L. Frommberger, N. Dethlefs, & H. Sahli (Eds.), Proceedings of ECAI’12 workshop on Machine Learning for Interactive Systems: Bridging the Gap between Language, Motor Control and Vision (Vol. 1, pp. 13-15). (Proceedings of ECAI’12 workshop on Machine Learning for Interactive Systems: Bridging the Gap between Language, Motor Control and Vision; No. 1).
@inproceedings{8965ee768f5c4a259fd9ce98db8f1844,
title = "Fast Learning-based Gesture Recognition for Child-robot Interactions",
abstract = "In this paper we propose a reliable gesture recognition system that could be run on low-level machines in real-time, which is practical in human-robot interaction scenarios. The system is based on a Random Forest classifier fed with Motion History Images(MHI) as classi?cation features. To detect fast continuous gestures as well as to improve the robustness, we introduce a feedback mechanism for parameter tuning. We applied the system as a component in the child-robot imitation game of ALIZ-E project.",
keywords = "Gesture Recognition,, Machine Learning, HRI",
author = "Weiyi Wang and Valentin Enescu and Hichem Sahli",
note = "Heriberto Cuay{\'a}huitl, Lutz Frommberger, Nina Dethlefs, Hichem Sahli; ECAI{\textquoteright}12 workshop on Machine Learning for Interactive Systems: Bridging the Gap between Language, Motor Control and Vision ; Conference date: 27-08-2012 Through 27-08-2012",
year = "2012",
month = aug,
day = "27",
language = "English",
volume = "1",
series = "Proceedings of ECAI{\textquoteright}12 workshop on Machine Learning for Interactive Systems: Bridging the Gap between Language, Motor Control and Vision",
number = "1",
pages = "13--15",
editor = "Heriberto Cuay{\'a}huitl and Lutz Frommberger and Nina Dethlefs and Hichem Sahli",
booktitle = "Proceedings of ECAI{\textquoteright}12 workshop on Machine Learning for Interactive Systems: Bridging the Gap between Language, Motor Control and Vision",
url = "http://www.sfbtr8.spatial-cognition.de/mlis-2012/Overview.html, http://www.sfbtr8.spatial-cognition.de/mlis-2012/Overview.html, http://www.sfbtr8.spatial-cognition.de/mlis-2012/Overview.html, http://www.sfbtr8.spatial-cognition.de/mlis-2012/Overview.html",
}