Drowsiness Detector using Python. College Project with all Documents
This is our Sem 5 mini project. It is about driver drowsiness. We measure if they are sleepy, drowsy or active(awake).
The OcuPi is an iteration of the previous OpenCLOS (deprecated) of the same author.
Very Simple Python-based drowsiness detection system by utilizing computer vision and facial landmarks to enhance road safety.
DriveSafe AI is a real-time driver drowsiness detection system that uses Python and OpenCV to monitor eye movements through a webcam. The system detects fatigue based on eye closure patterns and provides instant audio and visual alerts using a web-based interface.
EAR/MAR driver fatigue and yawn detection prototype
Real-Time Drowsiness Identification based on Eye State Analysis
This project is a Driver Monitoring System that detects drowsiness and mobile phone usage using YOLOv8, OpenCV, and Dlib. The system continuously monitors the driver's facial expressions and actions to ensure road safety.
A Python script that checks driver attentiveness using machine learning computer vision.
It is machine learning based model used for detecting the drowsiness of a person. Can be used in vehicles.
Real-time AI-based driver drowsiness detection using Python, OpenCV, and MediaPipe Face Mesh. Monitors eye closure using Eye Aspect Ratio (EAR) and triggers a custom alarm to improve driver safety.
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.
AI-powered driver drowsiness detection system using Computer Vision, OpenCV and CNNs.
A real-time face recognition and drowsiness detection system using Computer Vision, Deep Learning, and modern web technologies. Monitors eye state, gaze, and head pose to enhance safety by alerting users when signs of drowsiness or distraction are detected. Built with React, Tailwind CSS, Flask, OpenCV, MediaPipe, and Keras.
This is a python project for drowsiness detection.
Seven algorithms for detecting driver state from video: Rule-Based, SVM, Random Forest, MLP, CNN, LSTM, and TCN.
A Python-based computer vision project that detects drowsiness using facial landmarks.
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