A faster version of the usual drowsiness detectors built using OpenCV.
-
Updated
Feb 5, 2019 - Python
A faster version of the usual drowsiness detectors built using OpenCV.
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.
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.
Constantly monitors the driver and plays an alert sound to wake the driver up if he/she is drowsy
Experimental notebooks on blink detection problem by analizing it with simple thresholds, timeseries approach and a ml model.
Real-Time Drowsiness Identification based on Eye State Analysis
A Python-based computer vision project that detects drowsiness using facial landmarks.
This repository is for DDW(Driver Drowsiness Warning Computer Vision Project) using Facial Detectors(DLIB)
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.
Implementation of a system to detect fatigue using EAR (Eye Aspect Ratio).
Add a description, image, and links to the eye-aspect-ratio topic page so that developers can more easily learn about it.
To associate your repository with the eye-aspect-ratio topic, visit your repo's landing page and select "manage topics."