Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM)

In today’s world driver drowsiness is a major reason for fatal accidents of on road vehicles. Developing an automated, real-time drowsiness detection system is essential to provide accurate and timely alerts to the driver. In the proposed system, hybrid approach of CNN (Convolutional Neural Network)...

Full description

Saved in:
Bibliographic Details
Published in:Materials today : proceedings Vol. 45; pp. 2897 - 2901
Main Authors: Rajamohana, S.P., Radhika, E.G., Priya, S., Sangeetha, S.
Format: Journal Article
Language:English
Published: Elsevier Ltd 2021
Subjects:
CNN and BiLSTM
Euclidean algorithm
Eye dataset
Euclidean algorithm
Eye dataset
CNN and BiLSTM
ISSN:2214-7853, 2214-7853
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In today’s world driver drowsiness is a major reason for fatal accidents of on road vehicles. Developing an automated, real-time drowsiness detection system is essential to provide accurate and timely alerts to the driver. In the proposed system, hybrid approach of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long Term Dependencies) is used to detect the driver’s drowsiness. Video camera is used to track the facial image and eye blinks of the driver. The proposed system works in three main phases: In the First phase, driver's face image is Identified and observed using a web camera. In the Second phase, the eye image features are extracted using the Euclidean algorithm. During the third phase, the eye blinks are continually monitored. The final stage decides whether the measure in eye square is closed state or open state. When a driver falls asleep, there will be a warning message to alert the driver to prevent road accidents.
ISSN:2214-7853
2214-7853
DOI:10.1016/j.matpr.2020.11.898