Programs
- M. Tech. in Automotive Engineering -
- Fellowship In Interventional Pulmonology and Pulmonary Critical care - Fellowship
Publication Type : Conference Paper
Publisher : 2019 2nd International Conference on Intelligent Computing
Source : 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)
Url : https://ieeexplore.ieee.org/abstract/document/8993260
Keywords : computer vision,driver information systems,image recognition,learning (artificial intelligence),neural nets,road accidents,road safety,Deep Learning,Convolutional Neural Network,Object detection,Computer Vision
Campus : Amritapuri
School : School of Engineering
Department : Computer Science
Year : 2019
Abstract : Driver distraction is a foremost cause for motor vehicle accidents and incidents. Driving requires an intensive amount of concentration otherwise the results can be fatal. Yet, most motor vehicles have no system in place to assist the driver when he is feeling drowsy, fatigued or distracted. In this research, we developed a system which detects the driver's drowsiness by using deep learning and computer vision. Whenever the driver is not concentrating, an alarm ring's. Image recognition is made possible through a model called deep convolutional neural networks (CNN). CNN can achieve good performance even on difficult image recognition tasks. Convolutional Neural Networks is a class of artificial neural networks. In this research, we used state of the art model for estimating the position of the face and the eyes to help in the better detection and reduce false positives and false negatives.
Cite this Research Publication : Kusuma S, DivyaUdayan J, Aashay Sachdeva, Driver Distraction Detection using Deep Learning and Computer Vision International Conference on Intelligent Computing , Instrumentation and Control Technologies, ICICICT-2019