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Authors: Huei-Yung Lin 1 and Kai-Chun Tu 2

Affiliations: 1 Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106, Taiwan ; 2 Department of Electrical Engineering, National Chung Cheng University, Chiayi 621, Taiwan

Keyword(s): Fatigue Driving Detection, IR Images, Convolutional Neural Network, Action Recognition.

Abstract: Traffic accident is one of top ten causes of death, and fatigue driving is one of the major reasons. It usually reduces the driver’s concentration and reaction speed, and is especially dangerous in some situations at night. This works presents a real-time driving fatigue monitoring system. The proposed network architecture with Unbalanced Local CNNs can effectively draw attentions to different face regions according to driver’s states due to fatigue. Based on SlowFast, the recognition accuracy of our method on the IR image datasets is greatly improved compared to the original model. Moreover, an adversarial learning mechanism is incorporated to extract the common features of daytime RGB and nighttime IR images to increase the overall robustness. The experiments carried out on public datasets and road scene images have demonstrated the effectiveness of the proposed technique. The code is available at https://github.com/KaiChun-Tu/slowfastDrowsyDriver

CC BY-NC-ND 4.0

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Paper citation in several formats:
Lin, H. and Tu, K. (2023). Night Fatigue Driving Detection Technology Using Infrared Images and Convolutional Neural Networks. In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-652-1; ISSN 2184-495X, SciTePress, pages 273-280. DOI: 10.5220/0011847400003479

@conference{vehits23,
author={Huei{-}Yung Lin. and Kai{-}Chun Tu.},
title={Night Fatigue Driving Detection Technology Using Infrared Images and Convolutional Neural Networks},
booktitle={Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2023},
pages={273-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011847400003479},
isbn={978-989-758-652-1},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Night Fatigue Driving Detection Technology Using Infrared Images and Convolutional Neural Networks
SN - 978-989-758-652-1
IS - 2184-495X
AU - Lin, H.
AU - Tu, K.
PY - 2023
SP - 273
EP - 280
DO - 10.5220/0011847400003479
PB - SciTePress

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