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A real-time drowsiness detection system for drivers, which alerts the driver if they fall asleep due to fatigue while still driving. The computer vision algorithm used for the implementation uses a trifold approach to detect drowsiness, including the measurement of forward head tilt angle, measurement of eye aspect ratio (to detect closure of ey…
DNDS is a vehicle safety recommendation system that monitors the driver’s facial behaviour to detect the driver’s drowsiness and yawning. The system also monitors the road in front to detect the road lanes, the lane curvature, the vehicle centre offset, and objects of multiple classes on the road, such as humans, animals, and other vehicles, etc.
This repository contains Python code for generating a yawning detection model and using it to detect yawning instances from a live camera stream. The model architecture consists of convolutional and pooling layers, followed by fully connected layers.
In this repository you will find an efficient 'Real Time Driver Drowsiness Detection for an Intelligent Transportation System', that will work on various constraints like while wearing Eye Glasses, Mask etc.
The program can detect if someone is yawn or not. Using the Dlib library we can calculate the lip aspect ratio and give the conclusion whether the person is yawn or not
Implement a system that reduces accidents and keep the driver safe by detecting drowsiness ,yawning and seatbelt to alert the driver and by being able to control the car and park it in case the driver stays asleep or unconscious.
This software is a real-time drowsiness detection system that will constantly monitor the driver's eyelids and detect sleepiness patterns. If it detects any signs of drowsiness, it will ring an alarm in hopes of alerting the driver.