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Authors: Gabor Balazs 1 ; 2 ; Mateusz Chmurski 1 ; 3 ; Walter Stechele 2 and Mariusz Zubert 3

Affiliations: 1 Infineon Technologies AG, Am Campeon 1-15, Neubiberg, Germany ; 2 Technical University of Munich, Munich, Germany ; 3 Lodz University of Technology, Lodz, Poland

ISBN: 978-989-758-484-8

ISSN: 2184-433X

Keyword(s): Sensor Fusion, Gesture Recognition, Convolutional Neural Network, Radar, Time of Flight.

Abstract: The goal of hand gesture recognition based on time-of-flight and radar sensors is to enhance the human-machine interface, while taking care of privacy issues of camera sensors. Additionally, the system needs to be deployable on low-power edge devices for applicability in serial-produced vehicles. Recent advances show the capabilities of deep neural networks for gesture classification but they are still limited to high performance hardware. Embedded neural network accelerators are constrained in memory and supported operations. These limitations form an architectural design problem that is addressed in this work. Novel gesture classification networks are optimized for embedded deployment. The new approaches perform equally compared to high-performance neural networks with 3D convolutions, but need only 8.9% of the memory. These lightweight network architectures allow deployment on constrained embedded accelerator devices, thus enhancing human-machine interfaces.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Balazs, G.; Chmurski, M.; Stechele, W. and Zubert, M. (2021). Sensor Fusion Neural Networks for Gesture Recognition on Low-power Edge Devices. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8; ISSN 2184-433X, pages 141-150. DOI: 10.5220/0010234101410150

@conference{icaart21,
author={Gabor Balazs. and Mateusz Chmurski. and Walter Stechele. and Mariusz Zubert.},
title={Sensor Fusion Neural Networks for Gesture Recognition on Low-power Edge Devices},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={141-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010234101410150},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Sensor Fusion Neural Networks for Gesture Recognition on Low-power Edge Devices
SN - 978-989-758-484-8
IS - 2184-433X
AU - Balazs, G.
AU - Chmurski, M.
AU - Stechele, W.
AU - Zubert, M.
PY - 2021
SP - 141
EP - 150
DO - 10.5220/0010234101410150

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