HRI-Gestures: Gesture Recognition for Human-Robot Interaction

Avgi Kollakidou, Frederik Haarslev, Cagatay Odabasi, Leon Bodenhagen, Norbert Krüger

2022

Abstract

Most of people’s communication happens through body language and gestures. Gesture recognition in human-robot interaction is an unsolved problem which limits the possible communication between humans and robots in today’s applications. Gesture recognition can be considered as the same problem as action recognition which is largely solved by deep learning, however, current publicly available datasets do not contain many classes relevant to human-robot interaction. In order to address the problem, a human-robot interaction gesture dataset is therefore required. In this paper, we introduce HRI-Gestures, which includes 13600 instances of RGB and depth image sequences, and joint position files. A state of the art action recognition network is trained on relevant subsets of the dataset and achieve upwards of 96.9% accuracy. However, as the network is designed for the large-scale NTU RGB+D dataset, subpar performance is achieved on the full HRI-Gestures dataset. Further enhancement of gesture recognition is possible by tailored algorithms or extension of the dataset.

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Paper Citation


in Harvard Style

Kollakidou A., Haarslev F., Odabasi C., Bodenhagen L. and Krüger N. (2022). HRI-Gestures: Gesture Recognition for Human-Robot Interaction. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-555-5, pages 559-566. DOI: 10.5220/0010871200003124


in Bibtex Style

@conference{visapp22,
author={Avgi Kollakidou and Frederik Haarslev and Cagatay Odabasi and Leon Bodenhagen and Norbert Krüger},
title={HRI-Gestures: Gesture Recognition for Human-Robot Interaction},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={559-566},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010871200003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - HRI-Gestures: Gesture Recognition for Human-Robot Interaction
SN - 978-989-758-555-5
AU - Kollakidou A.
AU - Haarslev F.
AU - Odabasi C.
AU - Bodenhagen L.
AU - Krüger N.
PY - 2022
SP - 559
EP - 566
DO - 10.5220/0010871200003124