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Accident Prevention Using Eye Blinking and Head Movement

Published on April 2012 by Abhi R. Varma, Seema V. Arote, Chetna Bharti, Kuldeep Singh
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Emerging Trends in Computer Science and Information Technology (ETCSIT2012)
Foundation of Computer Science USA
ETCSIT - Number 4
April 2012
Authors: Abhi R. Varma, Seema V. Arote, Chetna Bharti, Kuldeep Singh
f1d7e6ac-2221-462a-9a6b-087969972e96

Abhi R. Varma, Seema V. Arote, Chetna Bharti, Kuldeep Singh . Accident Prevention Using Eye Blinking and Head Movement. Emerging Trends in Computer Science and Information Technology (ETCSIT2012). ETCSIT, 4 (April 2012), 31-35.

@article{
author = { Abhi R. Varma, Seema V. Arote, Chetna Bharti, Kuldeep Singh },
title = { Accident Prevention Using Eye Blinking and Head Movement },
journal = { Emerging Trends in Computer Science and Information Technology (ETCSIT2012) },
issue_date = { April 2012 },
volume = { ETCSIT },
number = { 4 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 31-35 },
numpages = 5,
url = { /proceedings/etcsit/number4/5985-1028/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Emerging Trends in Computer Science and Information Technology (ETCSIT2012)
%A Abhi R. Varma
%A Seema V. Arote
%A Chetna Bharti
%A Kuldeep Singh
%T Accident Prevention Using Eye Blinking and Head Movement
%J Emerging Trends in Computer Science and Information Technology (ETCSIT2012)
%@ 0975-8887
%V ETCSIT
%N 4
%P 31-35
%D 2012
%I International Journal of Computer Applications
Abstract

This paper describes a real-time online prototype driver-fatigue monitor. It uses remotely located charge-coupled-device cameras equipped with active infrared illuminators to acquire video images of the driver. Various visual cues that typically characterize the level of alertness of a person are extracted in real time and systematically combined to infer the fatigue level of the driver. The visual cues employed characterize eyelid movement, gaze movement, head movement, and facial expression. A probabilistic model is developed to model human fatigue and to predict fatigue based on the visual cues obtained. The simultaneous use of multiple visual cues and their systematic combination yields a much more robust and accurate fatigue characterization than using a single visual cue. This system was validated under real-life fatigue conditions with human subjects of different ethnic backgrounds, genders, and ages; with/without glasses; and under different illumination conditions. It was found to be reasonably robust, reliable, and accurate in fatigue characterization.

References
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  2. Boston University Computer Science Technical Report No. 2005-12 Real Time Eye Tracking and Blink Detection with USB Cameras Michael Chau and Margrit Betke, Computer Science Department Boston University Boston, MA 02215, USA { mikechau, betke@cs. bu. edu} May 12, 2005
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Index Terms

Computer Science
Information Sciences

Keywords

Driver Vigilance Human Fatigue Probabilistic Intelligence.