A real time, webcam based, driver attention state detection/monitoring system in Python3 using OpenCV and Mediapipe

Real-time drowsiness detection using Python, MediaPipe, and EAR to monitor driver fatigue and prevent accidents.

MVP for detecting drowsiness of driver using the eye lids of the driver, if the driver seems to be drowsy it gives an alarm

While drunk or drowsy people can’t react to stimuli efficiently in the environment, thus we intend to check for a verbal response from the driver upon detecting anomalous driving patterns. IMU tracker upon detecting frequent changes in acceleration and sharp turns triggers the voice assistant, checking up on the driver’s state and takes further …

IoT-based system for real-time driver drowsiness detection using ESP32 and cloud services for monitoring and alerts.

Driver Behaviour Analysis System (DBAS) is a ROS-based driver monitoring system utilizing OpenCV, Dlib, and YOLOv5 to detect and alert on drowsiness, device usage, and other behaviors during driving.

A real-time drowsiness and yawn detection system built with Python, OpenCV, and dlib to help prevent accidents caused by driver fatigue.

This repository contains source code in Arduino for the project-"Driver Safety Enhancement System", that was carried out in Hackathon: "Byte-Camp'19"

While drunk or drowsy people can’t react to stimuli efficiently in the environment, thus we intend to check for a verbal response from the driver upon detecting anomalous driving patterns. IMU tracker upon detecting frequent changes in acceleration and sharp turns triggers the voice assistant, checking up on the driver’s state and takes further …

Proyecto 1 - Programacion Concurrente Y Paralela

Real-time face and eye tracking using OpenCV/Deep Learning. Drowsiness detection through eye aspect ratio (EAR) and blink frequency. Instant audio/visual alerts when signs of fatigue are identified. Flexible deployment on PC or edge devices. Potential integration with IoT for vehicle safety systems.

A real-time driver fatigue detection system using computer vision. This project monitors eye movements to detect drowsiness and alerts the driver with a visual and audible warning when fatigue is detected. Built with OpenCV, dlib, and Python, it's designed for enhancing driver safety.

Real-time Android app to detect driver drowsiness using MediaPipe, trigger alarms, and send SOS alerts.

Safe Steer enhances driver safety by using sensors and a camera on the steering wheel to prevent distractions and alert drivers if they're drowsy or inattentive🚗✨.

A computer vision system that detects driver drowsiness from facial features using CNN and classical methods (EAR, MAR, SVM). This project aims to prevent accidents caused by fatigue by offering an early warning mechanism in real-time environments.