Distracted Driver Detection and Classification
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 4
Abstract
The number of road accidents due to distracted driving has been on a rise in the recent years. As per the Union Road Transport and Highways Ministry Report 2016, 17 people were killed each hour in India due to road accidents. This makes it imperative to take measures to curb the number of road fatalities. The major cause of these accidents is driver error. This paper proposes solution to detect the distraction of driver, thus averting the possible accidents. The use of different Convolutional Neural Network (CNN) models namely: Small CNN, VGG16, VGG19, Inception for classification of distracted drivers according to State Farm Distracted Driver Detection challenge on Kaggle are depicted in this paper. The deep learning library used for the purpose is Keras running on top of TensorFlow. Our best result is a categorical cross entropy loss of 0.899 on the validation set.
Authors and Affiliations
Prof. Pramila M. Chawan, Shreyas Satardekar, Dharmin Shah, Rohit Badugu, Abhishek Pawar
Keywords
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- EP ID EP394014
- DOI 10.9790/9622-0804036064.
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