The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'estimation de la somnolence du conducteur est l'une des tâches importantes pour prévenir les accidents de voiture. La plupart des approches sont des classifications binaires qui classent un conducteur comme étant significativement somnolent ou non. L'estimation de la somnolence à plusieurs niveaux, qui détecte non seulement une somnolence importante mais également une somnolence modérée, est utile pour rendre le système automobile plus sûr et plus confortable. Les approches existantes sont principalement basées sur des mesures temporelles conventionnelles qui extraient des informations temporelles liées aux états oculaires, et ces mesures se concentrent principalement sur la détection d'une somnolence significative pour la classification binaire. Pour l'estimation de la somnolence à plusieurs niveaux, nous proposons deux mesures temporelles, le temps moyen de fermeture des yeux (AECT) et le pourcentage doux de fermeture des paupières (Soft PERCLOS). Les approches existantes sont également basées sur un réseau neuronal convolutif (CNN) dans le domaine temporel en tant que modèles de réseaux neuronaux profonds, dont les couches sont liées séquentiellement. Le modèle de réseau extrait des fonctionnalités principalement axées sur la résolution mono-temporelle. Nous avons constaté que les fonctionnalités axées sur la résolution multi-temporelle sont efficaces pour l'estimation de la somnolence à plusieurs niveaux, et nous proposons un CNN dans le domaine temporel lié en parallèle pour extraire les fonctionnalités multi-temporelles. Nous avons collecté notre propre ensemble de données dans un environnement réel et évalué les méthodes proposées avec l'ensemble de données. Comparé aux mesures temporelles et aux modèles de réseau existants, notre système surpasse les approches existantes sur l'ensemble de données.
Kenta NISHIYUKI
OMRON Corporation,Chubu University
Jia-Yau SHIAU
National Taiwan University
Shigenori NAGAE
OMRON Corporation
Tomohiro YABUUCHI
OMRON Corporation
Koichi KINOSHITA
OMRON Corporation
Yuki HASEGAWA
OMRON Corporation
Takayoshi YAMASHITA
Chubu University
Hironobu FUJIYOSHI
Chubu University
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Kenta NISHIYUKI, Jia-Yau SHIAU, Shigenori NAGAE, Tomohiro YABUUCHI, Koichi KINOSHITA, Yuki HASEGAWA, Takayoshi YAMASHITA, Hironobu FUJIYOSHI, "Driver Drowsiness Estimation by Parallel Linked Time-Domain CNN with Novel Temporal Measures on Eye States" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 6, pp. 1276-1286, June 2020, doi: 10.1587/transinf.2019MVP0017.
Abstract: Driver drowsiness estimation is one of the important tasks for preventing car accidents. Most of the approaches are binary classification that classify a driver is significantly drowsy or not. Multi-level drowsiness estimation, that detects not only significant drowsiness but also moderate drowsiness, is helpful to a safer and more comfortable car system. Existing approaches are mostly based on conventional temporal measures which extract temporal information related to eye states, and these measures mainly focus on detecting significant drowsiness for binary classification. For multi-level drowsiness estimation, we propose two temporal measures, average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS). Existing approaches are also based on a time domain convolutional neural network (CNN) as deep neural network models, of which layers are linked sequentially. The network model extracts features mainly focusing on mono-temporal resolution. We found that features focusing on multi-temporal resolution are effective to multi-level drowsiness estimation, and we propose a parallel linked time-domain CNN to extract the multi-temporal features. We collected an own dataset in a real environment and evaluated the proposed methods with the dataset. Compared with existing temporal measures and network models, Our system outperforms the existing approaches on the dataset.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019MVP0017/_p
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@ARTICLE{e103-d_6_1276,
author={Kenta NISHIYUKI, Jia-Yau SHIAU, Shigenori NAGAE, Tomohiro YABUUCHI, Koichi KINOSHITA, Yuki HASEGAWA, Takayoshi YAMASHITA, Hironobu FUJIYOSHI, },
journal={IEICE TRANSACTIONS on Information},
title={Driver Drowsiness Estimation by Parallel Linked Time-Domain CNN with Novel Temporal Measures on Eye States},
year={2020},
volume={E103-D},
number={6},
pages={1276-1286},
abstract={Driver drowsiness estimation is one of the important tasks for preventing car accidents. Most of the approaches are binary classification that classify a driver is significantly drowsy or not. Multi-level drowsiness estimation, that detects not only significant drowsiness but also moderate drowsiness, is helpful to a safer and more comfortable car system. Existing approaches are mostly based on conventional temporal measures which extract temporal information related to eye states, and these measures mainly focus on detecting significant drowsiness for binary classification. For multi-level drowsiness estimation, we propose two temporal measures, average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS). Existing approaches are also based on a time domain convolutional neural network (CNN) as deep neural network models, of which layers are linked sequentially. The network model extracts features mainly focusing on mono-temporal resolution. We found that features focusing on multi-temporal resolution are effective to multi-level drowsiness estimation, and we propose a parallel linked time-domain CNN to extract the multi-temporal features. We collected an own dataset in a real environment and evaluated the proposed methods with the dataset. Compared with existing temporal measures and network models, Our system outperforms the existing approaches on the dataset.},
keywords={},
doi={10.1587/transinf.2019MVP0017},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Driver Drowsiness Estimation by Parallel Linked Time-Domain CNN with Novel Temporal Measures on Eye States
T2 - IEICE TRANSACTIONS on Information
SP - 1276
EP - 1286
AU - Kenta NISHIYUKI
AU - Jia-Yau SHIAU
AU - Shigenori NAGAE
AU - Tomohiro YABUUCHI
AU - Koichi KINOSHITA
AU - Yuki HASEGAWA
AU - Takayoshi YAMASHITA
AU - Hironobu FUJIYOSHI
PY - 2020
DO - 10.1587/transinf.2019MVP0017
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2020
AB - Driver drowsiness estimation is one of the important tasks for preventing car accidents. Most of the approaches are binary classification that classify a driver is significantly drowsy or not. Multi-level drowsiness estimation, that detects not only significant drowsiness but also moderate drowsiness, is helpful to a safer and more comfortable car system. Existing approaches are mostly based on conventional temporal measures which extract temporal information related to eye states, and these measures mainly focus on detecting significant drowsiness for binary classification. For multi-level drowsiness estimation, we propose two temporal measures, average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS). Existing approaches are also based on a time domain convolutional neural network (CNN) as deep neural network models, of which layers are linked sequentially. The network model extracts features mainly focusing on mono-temporal resolution. We found that features focusing on multi-temporal resolution are effective to multi-level drowsiness estimation, and we propose a parallel linked time-domain CNN to extract the multi-temporal features. We collected an own dataset in a real environment and evaluated the proposed methods with the dataset. Compared with existing temporal measures and network models, Our system outperforms the existing approaches on the dataset.
ER -