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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Anggaran mengantuk pemandu adalah salah satu tugas penting untuk mencegah kemalangan kereta. Kebanyakan pendekatan adalah klasifikasi binari yang mengklasifikasikan pemandu secara ketara mengantuk atau tidak. Anggaran mengantuk berbilang peringkat, yang mengesan bukan sahaja rasa mengantuk yang ketara tetapi juga rasa mengantuk yang sederhana, membantu sistem kereta yang lebih selamat dan selesa. Pendekatan sedia ada kebanyakannya berdasarkan langkah temporal konvensional yang mengekstrak maklumat temporal yang berkaitan dengan keadaan mata, dan langkah ini tertumpu terutamanya pada mengesan rasa mengantuk yang ketara untuk klasifikasi binari. Untuk anggaran mengantuk berbilang peringkat, kami mencadangkan dua ukuran temporal, purata masa tertutup mata (AECT) dan peratusan lembut penutupan kelopak mata (PERCLOS Lembut). Pendekatan sedia ada juga berdasarkan rangkaian neural convolutional domain masa (CNN) sebagai model rangkaian saraf dalam, yang mana lapisannya dipautkan secara berurutan. Model rangkaian mengekstrak ciri terutamanya memfokuskan pada resolusi mono-temporal. Kami mendapati bahawa ciri yang memfokuskan pada resolusi berbilang masa adalah berkesan untuk anggaran mengantuk berbilang peringkat dan kami mencadangkan CNN domain masa dipautkan selari untuk mengekstrak ciri berbilang temporal. Kami mengumpul set data sendiri dalam persekitaran sebenar dan menilai kaedah yang dicadangkan dengan set data. Berbanding dengan ukuran temporal dan model rangkaian sedia ada, sistem kami mengatasi pendekatan sedia ada pada set data.
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
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
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
Salinan
@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},}
Salinan
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 -