Real-time gaze estimator based on driver's head orientation for forward collision warning system

Sung Joo Lee, Jaeik Jo, Ho Gi Jung, Kang Ryoung Park, Jaihie Kim

Research output: Contribution to journalArticle

66 Citations (Scopus)

Abstract

This paper presents a vision-based real-time gaze zone estimator based on a driver's head orientation composed of yaw and pitch. Generally, vision-based methods are vulnerable to the wearing of eyeglasses and image variations between day and night. The proposed method is novel in the following four ways: First, the proposed method can work under both day and night conditions and is robust to facial image variation caused by eyeglasses because it only requires simple facial features and not specific features such as eyes, lip corners, and facial contours. Second, an ellipsoidal face model is proposed instead of a cylindrical face model to exactly determine a driver's yaw. Third, we propose new featuresthe normalized mean and the standard deviation of the horizontal edge projection histogramto reliably and rapidly estimate a driver's pitch. Fourth, the proposed method obtains an accurate gaze zone by using a support vector machine. Experimental results from 200000 images showed that the root mean square errors of the estimated yaw and pitch angles are below 7 under both daylight and nighttime conditions. Equivalent results were obtained for drivers with glasses or sunglasses, and 18 gaze zones were accurately estimated using the proposed gaze estimation method.

Original languageEnglish
Article number5688323
Pages (from-to)254-267
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Volume12
Issue number1
DOIs
Publication statusPublished - 2011 Mar 1

Fingerprint

Eyeglasses
Alarm systems
Mean square error
Support vector machines
Glass

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

@article{31e5c08b928643529722bd1678a62750,
title = "Real-time gaze estimator based on driver's head orientation for forward collision warning system",
abstract = "This paper presents a vision-based real-time gaze zone estimator based on a driver's head orientation composed of yaw and pitch. Generally, vision-based methods are vulnerable to the wearing of eyeglasses and image variations between day and night. The proposed method is novel in the following four ways: First, the proposed method can work under both day and night conditions and is robust to facial image variation caused by eyeglasses because it only requires simple facial features and not specific features such as eyes, lip corners, and facial contours. Second, an ellipsoidal face model is proposed instead of a cylindrical face model to exactly determine a driver's yaw. Third, we propose new featuresthe normalized mean and the standard deviation of the horizontal edge projection histogramto reliably and rapidly estimate a driver's pitch. Fourth, the proposed method obtains an accurate gaze zone by using a support vector machine. Experimental results from 200000 images showed that the root mean square errors of the estimated yaw and pitch angles are below 7 under both daylight and nighttime conditions. Equivalent results were obtained for drivers with glasses or sunglasses, and 18 gaze zones were accurately estimated using the proposed gaze estimation method.",
author = "Lee, {Sung Joo} and Jaeik Jo and Jung, {Ho Gi} and Park, {Kang Ryoung} and Jaihie Kim",
year = "2011",
month = "3",
day = "1",
doi = "10.1109/TITS.2010.2091503",
language = "English",
volume = "12",
pages = "254--267",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

Real-time gaze estimator based on driver's head orientation for forward collision warning system. / Lee, Sung Joo; Jo, Jaeik; Jung, Ho Gi; Park, Kang Ryoung; Kim, Jaihie.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 1, 5688323, 01.03.2011, p. 254-267.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Real-time gaze estimator based on driver's head orientation for forward collision warning system

AU - Lee, Sung Joo

AU - Jo, Jaeik

AU - Jung, Ho Gi

AU - Park, Kang Ryoung

AU - Kim, Jaihie

PY - 2011/3/1

Y1 - 2011/3/1

N2 - This paper presents a vision-based real-time gaze zone estimator based on a driver's head orientation composed of yaw and pitch. Generally, vision-based methods are vulnerable to the wearing of eyeglasses and image variations between day and night. The proposed method is novel in the following four ways: First, the proposed method can work under both day and night conditions and is robust to facial image variation caused by eyeglasses because it only requires simple facial features and not specific features such as eyes, lip corners, and facial contours. Second, an ellipsoidal face model is proposed instead of a cylindrical face model to exactly determine a driver's yaw. Third, we propose new featuresthe normalized mean and the standard deviation of the horizontal edge projection histogramto reliably and rapidly estimate a driver's pitch. Fourth, the proposed method obtains an accurate gaze zone by using a support vector machine. Experimental results from 200000 images showed that the root mean square errors of the estimated yaw and pitch angles are below 7 under both daylight and nighttime conditions. Equivalent results were obtained for drivers with glasses or sunglasses, and 18 gaze zones were accurately estimated using the proposed gaze estimation method.

AB - This paper presents a vision-based real-time gaze zone estimator based on a driver's head orientation composed of yaw and pitch. Generally, vision-based methods are vulnerable to the wearing of eyeglasses and image variations between day and night. The proposed method is novel in the following four ways: First, the proposed method can work under both day and night conditions and is robust to facial image variation caused by eyeglasses because it only requires simple facial features and not specific features such as eyes, lip corners, and facial contours. Second, an ellipsoidal face model is proposed instead of a cylindrical face model to exactly determine a driver's yaw. Third, we propose new featuresthe normalized mean and the standard deviation of the horizontal edge projection histogramto reliably and rapidly estimate a driver's pitch. Fourth, the proposed method obtains an accurate gaze zone by using a support vector machine. Experimental results from 200000 images showed that the root mean square errors of the estimated yaw and pitch angles are below 7 under both daylight and nighttime conditions. Equivalent results were obtained for drivers with glasses or sunglasses, and 18 gaze zones were accurately estimated using the proposed gaze estimation method.

UR - http://www.scopus.com/inward/record.url?scp=79952068730&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952068730&partnerID=8YFLogxK

U2 - 10.1109/TITS.2010.2091503

DO - 10.1109/TITS.2010.2091503

M3 - Article

VL - 12

SP - 254

EP - 267

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

IS - 1

M1 - 5688323

ER -