Look at the Driver, Look at the Road: No Distraction! No Accident!
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.
Cite
Text
Rezaei and Klette. "Look at the Driver, Look at the Road: No Distraction! No Accident!." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.24Markdown
[Rezaei and Klette. "Look at the Driver, Look at the Road: No Distraction! No Accident!." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/rezaei2014cvpr-look/) doi:10.1109/CVPR.2014.24BibTeX
@inproceedings{rezaei2014cvpr-look,
title = {{Look at the Driver, Look at the Road: No Distraction! No Accident!}},
author = {Rezaei, Mahdi and Klette, Reinhard},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2014},
doi = {10.1109/CVPR.2014.24},
url = {https://mlanthology.org/cvpr/2014/rezaei2014cvpr-look/}
}