by M Rezaei, R Klette
Abstract:
The paper proposes an advanced driver-assistance system that correlates the driver’s head pose to road hazards by analyzing both simultaneously. In particular, we aim at the prevention of rear-end crashes due to driver fatigue or distraction. We contribute by three novel ideas: Asymmetric appearance-modeling, 2D to 3D pose estimation enhanced by the introduced Fermat-point transform, and adaptation of Global Haar (GHaar) classifiers for vehicle detection under challenging lighting conditions. The system defines the driver’s direction of attention (in 6 degrees of freedom), yawning and head-nodding detection, as well as vehicle detection, and distance estimation. Having both road and driver’s behaviour information, and implementing a fuzzy fusion system, we develop an integrated framework to cover all of the above subjects. We provide real-time performance analysis for real-world driving scenarios.
Reference:
Look at the driver, look at the road: No distraction! No accident! (M Rezaei, R Klette), In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2014.
Bibtex Entry:
@inproceedings{rezaei2014lookaccident!,
author = "Rezaei, M and Klette, R",
booktitle = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
month = "Sep",
pages = "129--136",
publisher = "IEEE Computer Society",
title = "Look at the driver, look at the road: No distraction! No accident!",
year = "2014",
abstract = "The paper proposes an advanced driver-assistance system that correlates the driver's head pose to road hazards by analyzing both simultaneously. In particular, we aim at the prevention of rear-end crashes due to driver fatigue or distraction. We contribute by three novel ideas: Asymmetric appearance-modeling, 2D to 3D pose estimation enhanced by the introduced Fermat-point transform, and adaptation of Global Haar (GHaar) classifiers for vehicle detection under challenging lighting conditions. The system defines the driver's direction of attention (in 6 degrees of freedom), yawning and head-nodding detection, as well as vehicle detection, and distance estimation. Having both road and driver's behaviour information, and implementing a fuzzy fusion system, we develop an integrated framework to cover all of the above subjects. We provide real-time performance analysis for real-world driving scenarios.",
doi = "10.1109/CVPR.2014.24",
isbn = "9781479951178",
issn = "1063-6919",
keyword = "2D to 3D modelling",
keyword = "Driver behaviour monitoring",
keyword = "Head pose estimation",
keyword = "Road safety",
keyword = "Vehicle detection",
language = "eng",
conference = "CVPR",
day = "24",
}