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.

Python-based Fleet Management System with real-time automotive telematics: OBD-II diagnostics, GPS tracking, CAN bus decoding, DTC analysis, driver behavior monitoring, fuel analytics & live dashboard. Built with FastAPI + SQLAlchemy.

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 action to ensure his/her safety.

Convolutional Neural Network for predicting driver attention based on a front-facing image of the driver 🚘

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.

No description provided.

Jetson Nano Drowsiness Detection - SUSTech Project of CS324: Deep Learning in Fall 2025 - Score: 95/100

No description provided.

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.

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

Real-time driver drowsiness detection using OpenCV and MediaPipe with EAR and MAR based voice alerts

Proyecto 1 - Programacion Concurrente Y Paralela

Analyzing large-scale vehicle camera datasets using Convolutional Neural Networks to classify distracted driving behaviors and evaluate trends in driver position change for road safety applications.

Driver drowsiness detection using YOLOv8n face ROI, a CNN classifier, Grad-CAM explainability, and fuzzy risk mapping.

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.

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.

Real-time driver drowsiness detection using Python, MediaPipe and OpenCV

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

AI system that detects driver drowsiness in real time using computer vision

Real-time drowsiness detection system using AI, FastAPI backend, and React Native mobile app

Real-time drowsiness detection system using webcam eye tracking. Monitors Eye Aspect Ratio (EAR) via MediaPipe and triggers an alarm when eyes stay closed for too long.

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

A robust, real-time Driver Monitoring System (DMS) combining CNN texture analysis with MediaPipe geometric landmarks. Features hybrid scoring for fatigue and distraction detection. Peer-reviewed and presented at ADCOMSYS 2025.

Web-based driver drowsiness detection system using facial landmark analysis.