Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio

2019 ◽  
Author(s):  
Sukrit Mehta ◽  
Sharad Dadhich ◽  
Sahil Gumber ◽  
Arpita Jadhav Bhatt
Keyword(s):  
Aspect Ratio ◽  
Real Time ◽  
Detection System ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Eye Closure

scholarly journals Driver Drowsiness Detection System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 2270-2272
Author(s):  
Charan M
Keyword(s):  
Real Time ◽  
Web Application ◽  
Detection System ◽  
Smart Phone ◽  
Learning Approaches ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
The Face ◽  
Bounding Boxes ◽  
Traditional Image

We propose a Driver drowsiness detection system, the purposes of which are to prevent from dangerous cause and to avoid accidents. Since all the processes on image recognition performed on a smart phone, the system does not need to send images to a server and runs on an android smart phone in a real-time way. Automatic image-based recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches without human supervision real-time drowsiness detection. This model classifies whether the person’s eyes are opened or closed. To recognize the face, a user should have to adjust camera such a way that it covers his face first, and then the system starts recognition within the indicated bounding boxes. In addition, the system estimates the actions of the person. This recognition process is performed repeatedly about every second. We will implement this system as Web application effectively for real-time recognition.

Visual-based Real Time Driver Drowsiness Detection System Using CNN

2021 ◽  
Author(s):  
Jonathan Flores-Monroy ◽  
Mariko Nakano-Miyatake ◽  
Gabriel Sanchez-Perez ◽  
Hector Perez-Meana
Keyword(s):  
Real Time ◽  
Detection System ◽  
Drowsiness Detection ◽  
Driver Drowsiness

scholarly journals Driver Drowsiness Detection System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1226-1231
Author(s):  
Kiranmayee V
Keyword(s):  
Visual Information ◽  
Driver Assistance System ◽  
Detection System ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Driver Assistance ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Drugs And Alcohol ◽  
Eye Closure

Drowsiness of drivers are among the critical reasons for accidents. This can be a relatively smaller number still, as among the multiple causes that can lead to an accident. Drowsiness, in general, is not easy to measure unlike drugs and alcohol, which have tests and indicators that are available easily. In this paper, we are presenting a module for Advanced Driver Assistance System (ADAS) to reduce drowsiness related accidents. The system deals with automatic driver drowsiness detection based on visual information. We propose an algorithm to track, analyze and locate both the drivers eyes and face to measure PERCLOS, a scientifically supported measure of drowsiness asso- ciated with slow eye closure.

scholarly journals Driver Drowsiness Detection System using Machine Learning Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 990-993
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Raspberry Pi ◽  
Data Set ◽  
Drowsiness Detection ◽  
Driver Drowsiness

Road crashes are the most common forms of accidents and deaths worldwide, and the significant reasons for these accidents are usually drunken, drowsiness and reckless behaviour of the driver. According to the World Health Organization, road traffic injuries have risen to 1.25 billion worldwide, which makes driver drowsiness detection a major potential area to avert numerous sleep-induced road accidents. This project proposes an idea to detect drowsiness using machine learning algorithms, hence alarming the driver in real-time to prevent a collision. The model uses the Haar Cascade algorithm, along with the OpenCV library to monitor the real-time video of the driver and to detect the eyes of the driver. The system uses the Eye Aspect Ratio (EAR) concept to determine if the eyes are open or closed. We also feed a data-set file consisting of the facial features data-points to train the machine learning algorithm. The model inspects each frame of the video, which helps to recognize the state of the driver. Furthermore, a Raspberry Pi single-board computer, combined with a camera module and an alarm system, facilitates the project to emulate a compact drowsiness detection system suitable for different automobiles.

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