The application analyses an image or a frame in which the the detected face must have Eye Aspect Ratio (EAR) must be greater that the selected threshold value.
A faster version of the usual drowsiness detectors built using OpenCV.
Real-time driver drowsiness and distraction detection system. EU regulation C(2023)4523 compliant gaze zone detection.
Implementation of a system to detect fatigue using EAR (Eye Aspect Ratio).
Constantly monitors the driver and plays an alert sound to wake the driver up if he/she is drowsy
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.
Real-time driver drowsiness detection using Raspberry Pi
Build on Python using Computer Vision libraries of Python (Dlib and OpenCV). It detects and counts the number of eye blinks in a video. It works on the concept of Eye aspect ratio.
Real-time AI driver monitoring system using MediaPipe and Next.js to detect fatigue, yawning, and distraction to enhance road safety.
Real-time eye blink detection system that uses facial landmark detection and eye aspect ratio (EAR) to detect user blinks via webcam.
Real-Time Drowsiness Identification based on Eye State Analysis
Our Automated Driver Fatigue Detection and Emergency Pull-Over System! Leveraging image processing technology, this system aims to detect signs of driver fatigue and initiate emergency brakes to prevent potential accidents.
Drowsiness Detection: A Digital Signal Processing Approach
Real-time driver drowsiness detection using facial landmarks, EAR, MAR, and alerts
A real-time driver drowsiness detection system using computer vision to monitor eye movements. It alerts drivers with a buzzer when signs of fatigue are detected, helping prevent accidents.
A Python-based computer vision project that detects drowsiness using facial landmarks.
Real-time driver drowsiness detection using MediaPipe FaceMesh + CNN/BiLSTM hybrid. Detects eye closure, yawning and head pose via facial landmarks with personal calibration and audible alerts.
This project is a real-time drowsiness detection system that uses computer vision and facial landmark analysis to monitor a user's alertness. It primarily relies on calculating two key metrics from live video input: the Eye Aspect Ratio (EAR) and the Mouth Aspect Ratio (MAR).
This repository is for DDW(Driver Drowsiness Warning Computer Vision Project) using Facial Detectors(DLIB)
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