Camera-based Driver Drowsiness State Classification Using Logistic Regression Models

Mohamed Hedi Baccour, Frauke Driewer, Tim Schack, Enkelejda Kasneci

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

Drowsiness at the wheel is a major problem for traffic road safety. A drowsy driver suffers from decreased vigilance, increased reaction time and degraded decision-making ability, all of which have a huge impact on the driving performance. A driver monitoring system that warns the driver of his or her critical drowsiness state is a worthwhile contribution to traffic road safety. A drowsy driver typically exhibits some observable behaviors, such as eye blinking and head movements, that can be tracked using a camera. In this study, we analyze the potential of eye closure and head rotation signals, provided by a driver camera, to classify the driver's drowsiness state using logistic regression models. This analysis is based on a large dataset collected from 71 subjects in driving simulator experiments. A reliable and independent reference for drowsiness, however, is required in order to perform this analysis. For this purpose, we devise a methodology that merges several drowsiness monitoring approaches to construct a reliable reference for drowsiness. Furthermore, we describe our approach to extract eye blink and head rotation features. Ultimately, we design logistic regression classifiers and combine them using the one-vs-one binarization technique. Our approach achieves a global balanced validation accuracy of 72.7% on a three-class classification problem (awake, questionable and drowsy) by adopting a strict and rigorous evaluation scheme (i.e., leave-one-drive-out cross-validation).

Original languageEnglish
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2243-2250
Number of pages8
ISBN (Electronic)9781728185262
DOIs
StatePublished - 11 Oct 2020
Externally publishedYes
Event2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
Duration: 11 Oct 202014 Oct 2020

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2020-October
ISSN (Print)1062-922X

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Country/TerritoryCanada
CityToronto
Period11/10/2014/10/20

Keywords

  • driver camera
  • driver monitoring system
  • driver state classification
  • driving simulator
  • drowsiness
  • ground truth construction
  • logistic regression
  • machine learning

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