Detecting Method Applicable to Individual Features for Drivers' Drowsiness

Takahiro HAMADA; Kazumasa ADACHI; Tomoaki NAKANO; Shin YAMAMOTO

Detecting Method Applicable to Individual Features for Drivers' Drowsiness

Takahiro HAMADA, Kazumasa ADACHI, Tomoaki NAKANO, Shin YAMAMOTO

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It is inevitable for driver assist and warning systems to consider the drivers' state of consciousness. Drowsiness is one of the important factors in estimating the drivers' state of consciousness. A Method to extract the driver's initial stage of drowsiness was developed by means of the eyelid's opening relevant to each various characteristic of objects with motion pictures processing in the actual driving environment. The result was that an increase of the long eyelid closure time was the key factor in estimating the initial stage of drivers' drowsiness while driving. And the state of drowsiness could be presumed by checking the frequencies of long eyelid closure time per unit period.

Publication: IEICE TRANSACTIONS on Information Vol.E87-D No.1 pp.89-96

Publication Date: 2004/01/01

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Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)

Category: ITS

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Takahiro HAMADA, Kazumasa ADACHI, Tomoaki NAKANO, Shin YAMAMOTO, "Detecting Method Applicable to Individual Features for Drivers' Drowsiness" in IEICE TRANSACTIONS on Information, vol. E87-D, no. 1, pp. 89-96, January 2004, doi: .
Abstract: It is inevitable for driver assist and warning systems to consider the drivers' state of consciousness. Drowsiness is one of the important factors in estimating the drivers' state of consciousness. A Method to extract the driver's initial stage of drowsiness was developed by means of the eyelid's opening relevant to each various characteristic of objects with motion pictures processing in the actual driving environment. The result was that an increase of the long eyelid closure time was the key factor in estimating the initial stage of drivers' drowsiness while driving. And the state of drowsiness could be presumed by checking the frequencies of long eyelid closure time per unit period.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e87-d_1_89/_p

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@ARTICLE{e87-d_1_89,
author={Takahiro HAMADA, Kazumasa ADACHI, Tomoaki NAKANO, Shin YAMAMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Detecting Method Applicable to Individual Features for Drivers' Drowsiness},
year={2004},
volume={E87-D},
number={1},
pages={89-96},
abstract={It is inevitable for driver assist and warning systems to consider the drivers' state of consciousness. Drowsiness is one of the important factors in estimating the drivers' state of consciousness. A Method to extract the driver's initial stage of drowsiness was developed by means of the eyelid's opening relevant to each various characteristic of objects with motion pictures processing in the actual driving environment. The result was that an increase of the long eyelid closure time was the key factor in estimating the initial stage of drivers' drowsiness while driving. And the state of drowsiness could be presumed by checking the frequencies of long eyelid closure time per unit period.},
keywords={},
doi={},
ISSN={},
month={January},}

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TY - JOUR
TI - Detecting Method Applicable to Individual Features for Drivers' Drowsiness
T2 - IEICE TRANSACTIONS on Information
SP - 89
EP - 96
AU - Takahiro HAMADA
AU - Kazumasa ADACHI
AU - Tomoaki NAKANO
AU - Shin YAMAMOTO
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2004
AB - It is inevitable for driver assist and warning systems to consider the drivers' state of consciousness. Drowsiness is one of the important factors in estimating the drivers' state of consciousness. A Method to extract the driver's initial stage of drowsiness was developed by means of the eyelid's opening relevant to each various characteristic of objects with motion pictures processing in the actual driving environment. The result was that an increase of the long eyelid closure time was the key factor in estimating the initial stage of drivers' drowsiness while driving. And the state of drowsiness could be presumed by checking the frequencies of long eyelid closure time per unit period.
ER -