الفهرس 
Driver Distraction De nition and TypesOnly 14 pages are availabe for public view
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Abstract
It has been noticed over the past few years that driver distraction has been responsible
for good percentage of road accidents. Moreover, cell phone usage has
been reported to be a major cause for driver distraction-based road accidents.
These observations led governments to ban cell phone usage among distracted
drivers. The new regulations for banning cell phone usage while driving raised
the need for automated systems to monitor drivers and report cell phone usage
violations. Previous studies in this area show some systems for detecting driver
distraction based on computer vision techniques and other systems based on vehicle
behavior. Among the computer vision based systems, some are intended
for alerting distracted driver and hence are mounted inside the vehicles. While
others are roadside setup to report cell phone usage violations. We propose an
integrated system to automatically detect, track, and report distracted drivers.
Based on real time camera feed, our Convolutional Neural Networks (CNN) detect
vehicles drivers and hence classify their attention. An automatic license plate
recognition module records distracted drivers’ vehicles’ plate numbers and report
them through a simple web-based server component. To train and test our CNNs,
we collected and annotated various video footage of volunteering drivers from a
camera mounted on a patrol car as well as a roadside gantry. We collected two
ix
datasets for the roadside and patrol setups. Then, we trained a CNN based classi er
to classify drivers’ behavior as whether they are driving safely or using their
cell phones. Our system achieved 89% driver detections and 95% classi cation
accuracy on a recorded test set. We also report the results of one full day run
of the roadside setup which resulted in 100% recall of cell phone usage and 46% precision and discuss these results.