@article{(Open Science Index):https://publications.waset.org/pdf/15441,
	  title     = {Eye-Gesture Analysis for Driver Hazard Awareness},
	  author    = {Siti Nor Hafizah binti Mohd Zaid and  Mohamed Abdel-Maguid and  Abdel-Hamid Soliman},
	  country	= {},
	  institution	= {},
	  abstract     = {Because road traffic accidents are a major source of death worldwide, attempts have been made to create Advanced Driver Assistance Systems (ADAS) able to detect vehicle, driver and
environmental conditions that are cues for possible potential accidents. This paper presents continued work on a novel Nonintrusive
Intelligent Driver Assistance and Safety System (Ni-DASS)
for assessing driver attention and hazard awareness. It uses two onboard
CCD cameras – one observing the road and the other observing
the driver-s face. The windscreen is divided into cells and analysis of
the driver-s eye-gaze patterns allows Ni-DASS to determine the windscreen cell the driver is focusing on using eye-gesture templates.
Intersecting the driver-s field of view through the observed
windscreen cell with subsections of the camera-s field of view containing a potential hazard allows Ni-DASS to estimate the
probability that the driver has actually observed the hazard. Results
have shown that the proposed technique is an accurate enough
measure of driver observation to be useful in ADAS systems.},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {6},
	  number    = {5},
	  year      = {2012},
	  pages     = {618 - 624},
	  ee        = {https://publications.waset.org/pdf/15441},
	  url   	= {https://publications.waset.org/vol/65},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 65, 2012},
	}