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.
Experimental on-device Android app for driver drowsiness and road-scene awareness.
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.
SafeDrive - real-time driver drowsiness detection web app using YOLOv5 over webcam video, with audio alerts
MVP for detecting drowsiness of driver using the eye lids of the driver, if the driver seems to be drowsy it gives an alarm
Convolutional Neural Network for predicting driver attention based on a front-facing image of the driver 🚘
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 …
Real-time driver drowsiness detection using OpenCV Haar Cascades and a CNN trained on eye-state images. Triggers an audio alarm and saves a snapshot when prolonged eye closure is detected via webcam.
Jetson Nano Drowsiness Detection - SUSTech Project of CS324: Deep Learning in Fall 2025 - Score: 95/100
AURA — Real-time AI-powered drowsiness & fatigue monitoring system using MediaPipe computer vision, PERCLOS eye tracking, and intelligent multi-stage voice alerts.
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 driver monitoring and risk assessment system using YOLO, MediaPipe, and computer vision to detect driver drowsiness, distraction, phone usage, and other risky behaviors.
A software-simulated IoT microproject for an Anti-Sleep Driver Alert System. Built and runnable on Wokwi, it features a smart fatigue scoring engine tracking simulated eye blinks, vehicle speed scaling, and real I2C head-tilt data using an MPU6050 accelerometer with a 3-level alert routine.
Real-time driver fatigue monitoring system using OpenCV facial landmarks and eye aspect ratio (EAR)
AI-powered driver safety monitor — real-time drowsiness, gaze tracking, phone & smoke detection using YOLO11x, MediaPipe & HRNetV2. ESP32 IoT hardware alerts, Blynk app, full GPU acceleration.
Official JavaScript/TypeScript SDK for the Fleeta Open API
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