Vision-Based Driver’s Attention Monitoring System for Smart Vehicles

Lamia Alam; Mohammed Moshiul Hoque

doi:10.1007/978-3-030-00979-3_20

Vision-Based Driver’s Attention Monitoring System for Smart Vehicles

Lamia Alam
, Mohammed Moshiul Hoque

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

11 Scopus citations

Abstract

Recent studies revealed that the driver’s inattention is one of the most prominent reasons for car accidents. Intelligent driving assistant system with real time monitoring of the driver’s attentional status may reduce the accident rate that mostly occurred due to lack of attention. In this paper, we presents a vision-based driver’s attention monitoring system that estimates the driver’s attentional status in terms of four categories: attentive, distracted, drowsy, and fatigue respectively. The attentional status is classified with a variety of parameters such as, percentage of eyelid closure over time (PERCLOS), yawn frequency and gaze direction. Experimental results with different subjects show that the system can classify the driver’s attentional status with a reasonable accuracy.

Original language	English
Title of host publication	Intelligent Computing and Optimization
Editors	Pandian Vasant, Gerhard-Wilhelm Weber, Ivan Zelinka
Publisher	Springer Verlag
Pages	196-209
Number of pages	14
ISBN (Print)	9783030009786
DOIs	https://doi.org/10.1007/978-3-030-00979-3_20
State	Published - 2019
Externally published	Yes
Event	International Conference on Intelligent Computing and Optimization, ICO 2018 - Pattaya, Thailand Duration: Oct 4 2018 → Oct 5 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	866
ISSN (Print)	2194-5357

Conference

Conference	International Conference on Intelligent Computing and Optimization, ICO 2018
Country/Territory	Thailand
City	Pattaya
Period	10/4/18 → 10/5/18

Keywords

Attentional status
Computer vision
Gaze direction
Human computer interaction
Yawn frequency

Access to Document

10.1007/978-3-030-00979-3_20

Cite this

@inproceedings{96b5d9b88f9d4ebc9c1d773e91d7e71b,

title = "Vision-Based Driver{\textquoteright}s Attention Monitoring System for Smart Vehicles",

abstract = "Recent studies revealed that the driver{\textquoteright}s inattention is one of the most prominent reasons for car accidents. Intelligent driving assistant system with real time monitoring of the driver{\textquoteright}s attentional status may reduce the accident rate that mostly occurred due to lack of attention. In this paper, we presents a vision-based driver{\textquoteright}s attention monitoring system that estimates the driver{\textquoteright}s attentional status in terms of four categories: attentive, distracted, drowsy, and fatigue respectively. The attentional status is classified with a variety of parameters such as, percentage of eyelid closure over time (PERCLOS), yawn frequency and gaze direction. Experimental results with different subjects show that the system can classify the driver{\textquoteright}s attentional status with a reasonable accuracy.",

keywords = "Attentional status, Computer vision, Gaze direction, Human computer interaction, Yawn frequency",

author = "Lamia Alam and Hoque, \{Mohammed Moshiul\}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; International Conference on Intelligent Computing and Optimization, ICO 2018 ; Conference date: 04-10-2018 Through 05-10-2018",

year = "2019",

doi = "10.1007/978-3-030-00979-3\_20",

language = "English",

isbn = "9783030009786",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "196--209",

editor = "Pandian Vasant and Gerhard-Wilhelm Weber and Ivan Zelinka",

booktitle = "Intelligent Computing and Optimization",

}

Alam, L & Hoque, MM 2019, Vision-Based Driver’s Attention Monitoring System for Smart Vehicles. in P Vasant, G-W Weber & I Zelinka (eds), Intelligent Computing and Optimization. Advances in Intelligent Systems and Computing, vol. 866, Springer Verlag, pp. 196-209, International Conference on Intelligent Computing and Optimization, ICO 2018, Pattaya, Thailand, 10/4/18. https://doi.org/10.1007/978-3-030-00979-3_20

Vision-Based Driver’s Attention Monitoring System for Smart Vehicles. / Alam, Lamia; Hoque, Mohammed Moshiul.
Intelligent Computing and Optimization. ed. / Pandian Vasant; Gerhard-Wilhelm Weber; Ivan Zelinka. Springer Verlag, 2019. p. 196-209 (Advances in Intelligent Systems and Computing; Vol. 866).

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

TY - GEN

T1 - Vision-Based Driver’s Attention Monitoring System for Smart Vehicles

AU - Alam, Lamia

AU - Hoque, Mohammed Moshiul

PY - 2019

Y1 - 2019

N2 - Recent studies revealed that the driver’s inattention is one of the most prominent reasons for car accidents. Intelligent driving assistant system with real time monitoring of the driver’s attentional status may reduce the accident rate that mostly occurred due to lack of attention. In this paper, we presents a vision-based driver’s attention monitoring system that estimates the driver’s attentional status in terms of four categories: attentive, distracted, drowsy, and fatigue respectively. The attentional status is classified with a variety of parameters such as, percentage of eyelid closure over time (PERCLOS), yawn frequency and gaze direction. Experimental results with different subjects show that the system can classify the driver’s attentional status with a reasonable accuracy.

AB - Recent studies revealed that the driver’s inattention is one of the most prominent reasons for car accidents. Intelligent driving assistant system with real time monitoring of the driver’s attentional status may reduce the accident rate that mostly occurred due to lack of attention. In this paper, we presents a vision-based driver’s attention monitoring system that estimates the driver’s attentional status in terms of four categories: attentive, distracted, drowsy, and fatigue respectively. The attentional status is classified with a variety of parameters such as, percentage of eyelid closure over time (PERCLOS), yawn frequency and gaze direction. Experimental results with different subjects show that the system can classify the driver’s attentional status with a reasonable accuracy.

KW - Attentional status

KW - Computer vision

KW - Gaze direction

KW - Human computer interaction

KW - Yawn frequency

UR - https://www.scopus.com/pages/publications/85054736900

U2 - 10.1007/978-3-030-00979-3_20

DO - 10.1007/978-3-030-00979-3_20

M3 - Conference contribution

AN - SCOPUS:85054736900

SN - 9783030009786

T3 - Advances in Intelligent Systems and Computing

SP - 196

EP - 209

BT - Intelligent Computing and Optimization

A2 - Vasant, Pandian

A2 - Weber, Gerhard-Wilhelm

A2 - Zelinka, Ivan

PB - Springer Verlag

T2 - International Conference on Intelligent Computing and Optimization, ICO 2018

Y2 - 4 October 2018 through 5 October 2018

ER -

Vision-Based Driver’s Attention Monitoring System for Smart Vehicles

Abstract

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Keywords

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