by Rezaei, M and Klette, R
Abstract:
The design of intelligent driver assistance systems is of increasing importance for the vehicle-producing industry and road-safety solutions. This article starts with a review of road-situation monitoring and driver’s behaviour analysis. This article also discusses lane tracking using vision (or other) sensors, and the strength or weakness of different methods of driver behaviour analysis (e.g. iris or pupil status monitoring, and EEG spectrum analysis). This article focuses then on image analysis techniques and develops a multi-faceted approach in order to analyse driver’s face and eye status via implementing a real-time AdaBoost cascade classifier with Haar-like features. The proposed method is tested in a research vehicle for driver distraction detection using a binocular camera. The developed algorithm is robust in detecting different types of driver distraction such as drowsiness, fatigue, drunk driving or the performance of secondary tasks. © 2011 Taylor & Francis.
Reference:
Simultaneous analysis of driver behaviour and road condition for driver distraction detection (Rezaei, M and Klette, R), In International Journal of Image and Data Fusion, volume 2, 2011.
Bibtex Entry:
@article{rezaei2011simultaneousdetection,
author = "Rezaei, M and Klette, R",
journal = "International Journal of Image and Data Fusion",
month = "Sep",
pages = "217--236",
title = "Simultaneous analysis of driver behaviour and road condition for driver distraction detection",
volume = "2",
year = "2011",
abstract = "The design of intelligent driver assistance systems is of increasing importance for the vehicle-producing industry and road-safety solutions. This article starts with a review of road-situation monitoring and driver's behaviour analysis. This article also discusses lane tracking using vision (or other) sensors, and the strength or weakness of different methods of driver behaviour analysis (e.g. iris or pupil status monitoring, and EEG spectrum analysis). This article focuses then on image analysis techniques and develops a multi-faceted approach in order to analyse driver's face and eye status via implementing a real-time AdaBoost cascade classifier with Haar-like features. The proposed method is tested in a research vehicle for driver distraction detection using a binocular camera. The developed algorithm is robust in detecting different types of driver distraction such as drowsiness, fatigue, drunk driving or the performance of secondary tasks. © 2011 Taylor \& Francis.",
doi = "10.1080/19479832.2011.590458",
issn = "1947-9832",
eissn = "1947-9824",
issue = "3",
keyword = "Cascaded classifier",
keyword = "Driver assistance systems",
keyword = "Driver distraction detection",
keyword = "Face and eye detection",
keyword = "Haar-like features",
language = "eng",
}