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Authors: Zorana Doždor ; Tomislav Hrkać and Zoran Kalafatić

Affiliation: University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia

Keyword(s): Recurrent Neural Network, Gated Recurrent Unit, Online Gesture Recognition, Hand Skeleton, Sliding Window.

Abstract: Hand gesture recognition from skeleton data has recently gained popularity due to the broad areas of application and availability of adequate input devices. However, before utilising this technology in real-world conditions there are still many challenges left to overcome. A major challenge is robust gesture localization – estimating the beginning and the end of a gesture in online conditions. We propose an online gesture detection system based on two models – one for gesture localization and the other for gesture classification. This approach is tested and compared against the one-model approach, often found in literature. The system is evaluated on the recent SHREC challenge which offers datasets for online gesture detection. Results show the benefits of distributing the tasks of localization and recognition instead of using one model for both tasks. The proposed system obtains state-of-the-art results on SHREC gesture detection dataset.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Doždor, Z. ; Hrkać, T. and Kalafatić, Z. (2023). Two-Model-Based Online Hand Gesture Recognition from Skeleton Data. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 838-845. DOI: 10.5220/0011663200003417

@conference{visapp23,
author={Zorana Doždor and Tomislav Hrkać and Zoran Kalafatić},
title={Two-Model-Based Online Hand Gesture Recognition from Skeleton Data},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={838-845},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011663200003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Two-Model-Based Online Hand Gesture Recognition from Skeleton Data
SN - 978-989-758-634-7
IS - 2184-4321
AU - Doždor, Z.
AU - Hrkać, T.
AU - Kalafatić, Z.
PY - 2023
SP - 838
EP - 845
DO - 10.5220/0011663200003417
PB - SciTePress

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