Popular GitHub Repositories for Eye Tracking

A webcam based pupil tracking implementation.

? Use machine learning in JavaScript to detect eye movements and build gaze-controlled experiences.

Creating a software for automatic monitoring in online proctoring

Real time eye tracking for embedded and mobile devices.

This is a AI Based Smart Exam Proctoring System using python flask, mysql as database, yolov4

A deep learning based gaze estimation framework implemented with PyTorch

Gaze tracking

Neural Action is a real-time CNN-based gaze tracking application providing human-machine interface to improve accessibility.

Psychology-inspired Eye Movement Synthesis for Gaze-based Activity Recognition

Modular components for real-time mobile eye tracking.

Webcam eye tracker project. Created using PyTorch

Online Examination Cheating Detection Application with face recognition and eye tracking

ArtaEye is web/mobile camera eye tracking to make beautiful drawing and artwork based on eye movements. In this project. We target people with disabilities to create an artwork. We try to translate their feelings through their eyes into digital drawings.

Transform your webcam to an eye tracker

real-time cpu only eye-tracking using a pretrained yolov8n face detection model and a custom finetuned yolov8n-pose model for the eyes. supports both live webcam feed and video files.

Detect if the driver is feeling drowsy and sound an alarm on doing so to wake him/her up.

Android application able to recognize, with the help of two neural networks, the eyes and pupils of the faces captured by the phone's camera.

Real-time driver drowsiness detection system using computer vision, OpenCV, and MediaPipe face mesh. Monitors eye movements and calculates Eye Aspect Ratio (EAR) to detect fatigue, providing immediate visual alerts for enhanced driving safety.

This is repository is for AIN311 Course Project "Eye Tracking and Prior Knowledge"

Drowsiness detection system using EAR and MAR (Eye and Mouth Aspect Ratios) , dlib used to extract facial landmarks , drowsiness detected using eye closure rates and yawn counts.