A Review Approach For Detecting Distracted Driver
Journal Title: International Journal of Engineering and Science Invention - Year 2018, Vol 7, Issue 10
Abstract
Distracted driving causes a great loss of life as well as money. According to the National Highway Traffic Safety Administration (NHTSA), in 2015 alone, 3477 people were killed, and 391000 were injured in motor vehicle crashes involving distracted drivers. To address this problem various models of Deep Learning like Support Vector Machines (SVM), Convolutional Neural Networks (CNN), DeepCNN are used to detect the distracted driving. Out of these models using the VGG-16+INCEPTION V4+KNN architecture in basic CNN was found to have the highest accuracy. Here we have proposed a better model RNN which gives a better accuracy and also solves the problem that were faced by the traditional methods and CNN by providing images at regular intervals and not only specifically considering the image at a particular instance.
Authors and Affiliations
Parth Sayani, Vyom Unadkat, Harshal Kapadia, Prasham Shah
Keywords
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