SYSTEM FOR DETECTING AND REPORTING CELL PHONE DISTRACTED DRIVERS

According to published statistics, driver distraction by cell phone usage has recently become one of the main causes of fatal road accidents. We propose an integrated system to automatically detect, track, and report distracted drivers to help saving lives. 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. Our system achieved 89% and 95% detection and classification accuracy respectively on the recorded test set.