This paper is published in Volume-4, Issue-2, 2018
Area
Deep Learning
Author
Syed Thoufich, Nikhil V Anand, Rajaganapathi S, Roopesh Kumar B N
Org/Univ
Kammavari Sangha Institute of Technology, Bengaluru, Karnataka, India
Keywords
Deep Learning, Convolution Neural Network, Image Classification, Distraction.
Citations
IEEE
Syed Thoufich, Nikhil V Anand, Rajaganapathi S, Roopesh Kumar B N. Distracted driver detection using convolutional neural network, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Syed Thoufich, Nikhil V Anand, Rajaganapathi S, Roopesh Kumar B N (2018). Distracted driver detection using convolutional neural network. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2) www.IJARIIT.com.
MLA
Syed Thoufich, Nikhil V Anand, Rajaganapathi S, Roopesh Kumar B N. "Distracted driver detection using convolutional neural network." International Journal of Advance Research, Ideas and Innovations in Technology 4.2 (2018). www.IJARIIT.com.
Syed Thoufich, Nikhil V Anand, Rajaganapathi S, Roopesh Kumar B N. Distracted driver detection using convolutional neural network, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Syed Thoufich, Nikhil V Anand, Rajaganapathi S, Roopesh Kumar B N (2018). Distracted driver detection using convolutional neural network. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2) www.IJARIIT.com.
MLA
Syed Thoufich, Nikhil V Anand, Rajaganapathi S, Roopesh Kumar B N. "Distracted driver detection using convolutional neural network." International Journal of Advance Research, Ideas and Innovations in Technology 4.2 (2018). www.IJARIIT.com.
Abstract
In order to help in reducing possible accidents caused due to a distraction while driving. A model is proposed based on the convolution neural network. The Convolution Neural Network is commonly used the model in deep learning. Among various types of networks, convolution neural network resulted in high performance on image classification. Here we build and train the model with a dataset of dashboard camera images of a driver, which detects the pre-defined set of actions of the driver engaging in an unsound behavior. And provides the suitable notification indicating the driver of his/her hindrance.
