Subspace eyetracking for driver warning

Fernando De la Torre, Carlos Javier Garcia Rubio, Elisa Martínez

Research output: A Conference proceeding or a Chapter in BookConference contribution

5 Citations (Scopus)

Abstract

Driver's fatigue/distraction is one of the most common causes of traffic accidents. The aim of this paper is to develop a real time system to detect anomalous situations while driving. In a learning stage, the user will sit in front of the camera and the system will learn a person-specific facial appearance model (PSFAM) in an automatic manner. The PSFAM will be used to perform gaze detection and eye-activity recognition in a real time based on subspace constraints. Preliminary experiments measuring the PERCLOS index (average time that the eyes are closed) under a variety of conditions are reported.

Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing
PublisherIEEE
Pages329-332
Number of pages4
Volume3
ISBN (Print)9780780377508
DOIs
Publication statusPublished - 14 Sep 2003
Externally publishedYes
EventProceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain
Duration: 14 Sep 200317 Sep 2003

Conference

ConferenceProceedings: 2003 International Conference on Image Processing, ICIP-2003
CountrySpain
CityBarcelona
Period14/09/0317/09/03

Fingerprint

Highway accidents
Real time systems
Cameras
Fatigue of materials
Experiments

Cite this

De la Torre, F., Rubio, C. J. G., & Martínez, E. (2003). Subspace eyetracking for driver warning. In IEEE International Conference on Image Processing (Vol. 3, pp. 329-332). IEEE. https://doi.org/10.1109/ICIP.2003.1247248
De la Torre, Fernando ; Rubio, Carlos Javier Garcia ; Martínez, Elisa. / Subspace eyetracking for driver warning. IEEE International Conference on Image Processing. Vol. 3 IEEE, 2003. pp. 329-332
@inproceedings{7f9c76b8c5d64ab287846652d398eed1,
title = "Subspace eyetracking for driver warning",
abstract = "Driver's fatigue/distraction is one of the most common causes of traffic accidents. The aim of this paper is to develop a real time system to detect anomalous situations while driving. In a learning stage, the user will sit in front of the camera and the system will learn a person-specific facial appearance model (PSFAM) in an automatic manner. The PSFAM will be used to perform gaze detection and eye-activity recognition in a real time based on subspace constraints. Preliminary experiments measuring the PERCLOS index (average time that the eyes are closed) under a variety of conditions are reported.",
keywords = "Eye movements, Highway accidents, Human computer interaction, Learning systems, Optical flows, Principal component analysis, Real time systems",
author = "{De la Torre}, Fernando and Rubio, {Carlos Javier Garcia} and Elisa Mart{\'i}nez",
year = "2003",
month = "9",
day = "14",
doi = "10.1109/ICIP.2003.1247248",
language = "English",
isbn = "9780780377508",
volume = "3",
pages = "329--332",
booktitle = "IEEE International Conference on Image Processing",
publisher = "IEEE",

}

De la Torre, F, Rubio, CJG & Martínez, E 2003, Subspace eyetracking for driver warning. in IEEE International Conference on Image Processing. vol. 3, IEEE, pp. 329-332, Proceedings: 2003 International Conference on Image Processing, ICIP-2003, Barcelona, Spain, 14/09/03. https://doi.org/10.1109/ICIP.2003.1247248

Subspace eyetracking for driver warning. / De la Torre, Fernando; Rubio, Carlos Javier Garcia; Martínez, Elisa.

IEEE International Conference on Image Processing. Vol. 3 IEEE, 2003. p. 329-332.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Subspace eyetracking for driver warning

AU - De la Torre, Fernando

AU - Rubio, Carlos Javier Garcia

AU - Martínez, Elisa

PY - 2003/9/14

Y1 - 2003/9/14

N2 - Driver's fatigue/distraction is one of the most common causes of traffic accidents. The aim of this paper is to develop a real time system to detect anomalous situations while driving. In a learning stage, the user will sit in front of the camera and the system will learn a person-specific facial appearance model (PSFAM) in an automatic manner. The PSFAM will be used to perform gaze detection and eye-activity recognition in a real time based on subspace constraints. Preliminary experiments measuring the PERCLOS index (average time that the eyes are closed) under a variety of conditions are reported.

AB - Driver's fatigue/distraction is one of the most common causes of traffic accidents. The aim of this paper is to develop a real time system to detect anomalous situations while driving. In a learning stage, the user will sit in front of the camera and the system will learn a person-specific facial appearance model (PSFAM) in an automatic manner. The PSFAM will be used to perform gaze detection and eye-activity recognition in a real time based on subspace constraints. Preliminary experiments measuring the PERCLOS index (average time that the eyes are closed) under a variety of conditions are reported.

KW - Eye movements

KW - Highway accidents

KW - Human computer interaction

KW - Learning systems

KW - Optical flows

KW - Principal component analysis

KW - Real time systems

UR - http://www.scopus.com/inward/record.url?scp=0344703608&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2003.1247248

DO - 10.1109/ICIP.2003.1247248

M3 - Conference contribution

SN - 9780780377508

VL - 3

SP - 329

EP - 332

BT - IEEE International Conference on Image Processing

PB - IEEE

ER -

De la Torre F, Rubio CJG, Martínez E. Subspace eyetracking for driver warning. In IEEE International Conference on Image Processing. Vol. 3. IEEE. 2003. p. 329-332 https://doi.org/10.1109/ICIP.2003.1247248