Real-time vision-based driver drowsiness/fatigue detection system

K. P. Yao; W. H. Lin; C. Y. Fang; J. M. Wang; S. L. Chang; S. W. Chen

doi:10.1109/VETECS.2010.5493972

Real-time vision-based driver drowsiness/fatigue detection system

K. P. Yao, W. H. Lin, C. Y. Fang, J. M. Wang, S. L. Chang, S. W. Chen

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Citations (Scopus)

Abstract

In this paper, a vision system for monitoring driver's vigilance is presented. The level of vigilance is determined by integrating a number of facial parameters. In order to estimate these parameters, the facial features of eyes, mouth and head are first located in the input video sequence. The located facial features are then tracked over the subsequent images. Facial parameters are estimated during facial feature tracking. The estimated parametric values are collected and analyzed every fixed time interval to provide a real-time vigilance level of the driver. A series of experiments on real sequences were demonstrated to reveal the feasibility of the proposed system.

Original language	English
Title of host publication	2010 IEEE 71st Vehicular Technology
DOIs	https://doi.org/10.1109/VETECS.2010.5493972
Publication status	Published - 2010
Event	2010 IEEE 71st Vehicular Technology Conference, VTC 2010-Spring - Taipei, Taiwan Duration: 2010 May 16 → 2010 May 19

Other

Other	2010 IEEE 71st Vehicular Technology Conference, VTC 2010-Spring
Country	Taiwan
City	Taipei
Period	2010/05/16 → 2010/05/19

Keywords

Driver's vigilance monitoring
Facial parameters
Fuzzy reasoning
In-vehicle vision system

ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Science Applications
Applied Mathematics

Access to Document

10.1109/VETECS.2010.5493972

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Cite this

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title = "Real-time vision-based driver drowsiness/fatigue detection system",

abstract = "In this paper, a vision system for monitoring driver's vigilance is presented. The level of vigilance is determined by integrating a number of facial parameters. In order to estimate these parameters, the facial features of eyes, mouth and head are first located in the input video sequence. The located facial features are then tracked over the subsequent images. Facial parameters are estimated during facial feature tracking. The estimated parametric values are collected and analyzed every fixed time interval to provide a real-time vigilance level of the driver. A series of experiments on real sequences were demonstrated to reveal the feasibility of the proposed system.",

keywords = "Driver's vigilance monitoring, Facial parameters, Fuzzy reasoning, In-vehicle vision system",

author = "Yao, {K. P.} and Lin, {W. H.} and Fang, {C. Y.} and Wang, {J. M.} and Chang, {S. L.} and Chen, {S. W.}",

year = "2010",

doi = "10.1109/VETECS.2010.5493972",

language = "English",

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note = "2010 IEEE 71st Vehicular Technology Conference, VTC 2010-Spring ; Conference date: 16-05-2010 Through 19-05-2010",

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AU - Yao, K. P.

AU - Lin, W. H.

AU - Fang, C. Y.

AU - Wang, J. M.

AU - Chang, S. L.

AU - Chen, S. W.

PY - 2010

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N2 - In this paper, a vision system for monitoring driver's vigilance is presented. The level of vigilance is determined by integrating a number of facial parameters. In order to estimate these parameters, the facial features of eyes, mouth and head are first located in the input video sequence. The located facial features are then tracked over the subsequent images. Facial parameters are estimated during facial feature tracking. The estimated parametric values are collected and analyzed every fixed time interval to provide a real-time vigilance level of the driver. A series of experiments on real sequences were demonstrated to reveal the feasibility of the proposed system.

AB - In this paper, a vision system for monitoring driver's vigilance is presented. The level of vigilance is determined by integrating a number of facial parameters. In order to estimate these parameters, the facial features of eyes, mouth and head are first located in the input video sequence. The located facial features are then tracked over the subsequent images. Facial parameters are estimated during facial feature tracking. The estimated parametric values are collected and analyzed every fixed time interval to provide a real-time vigilance level of the driver. A series of experiments on real sequences were demonstrated to reveal the feasibility of the proposed system.

KW - Driver's vigilance monitoring

KW - Facial parameters

KW - Fuzzy reasoning

KW - In-vehicle vision system

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