REAL-TIME DROWSINESS DETECTION (RTD2)
In the present era, where the roads are filled with vehicles all the time, accidents are prone to happen. Studies show that over 50% of these road accidents in a year, are due to driver drowsiness and fatigue. Many attempts have been made to develop a solution to this problem to detect when the driver is drowsy, but everything has its own set of limitations. Our project aims to solve this problem by detecting the drowsiness of the driver and sending an alert. We propose to use the Dlib Library to detect the driver’s face, and a number of landmarks are plotted on the entire face of the driver. Using these landmarks, various calculations and comparisons take place to check whether the drivers’ eyes are closed or open, and whether the driver is yawning or not. From this, the system determines whether the driver is drowsy or not. If the driver is found to be drowsy, an audio and visual alert is triggered to alert the driver. With this project, we aim to help society by reducing road accidents, and saving lives and property.
Code (1)
Similar Papers 제목 키워드 기반
A Survey on Drowsiness Detection -- Modern Applications and Methods
Drowsiness detection holds paramount importance in ensuring safety in workplaces or behind the wheel, enhancing productivity, and healthcare across diverse domains. Therefore accurate and real-time drowsiness detection p…
Model CompressionSurveyReal-Time Drowsiness Detection Using Eye Aspect Ratio and Facial Landmark Detection
Drowsiness detection is essential for improving safety in areas such as transportation and workplace health. This study presents a real-time system designed to detect drowsiness using the Eye Aspect Ratio (EAR) and facia…
Facial Landmark DetectionReal-Time Drivers' Drowsiness Detection and Analysis through Deep Learning
A long road trip is fun for drivers. However, a long drive for days can be tedious for a driver to accommodate stringent deadlines to reach distant destinations. Such a scenario forces drivers to drive extra miles, utili…
Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers
Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to de…
SpecificityVigilEye -- Artificial Intelligence-based Real-time Driver Drowsiness Detection
This study presents a novel driver drowsiness detection system that combines deep learning techniques with the OpenCV framework. The system utilises facial landmarks extracted from the driver's face as input to Convoluti…
Deep LearningSpecificity