AI-powered Driver Drowsiness Detection System using Computer Vision & Machine Learning for real-time driver alertness monitoring and accident prevention.

Driver Monitoring System by using deep learning model Gaze, Face detection, Face Landmark, and Head pose estimation.

A driver monitoring system.

Real-time AI Driver Drowsiness Detection using Flask, OpenCV & Dlib with live browser camera monitoring and fatigue alerts.

VisionSense is a comprehensive ROS2-based computer vision system designed for autonomous vehicles running on NVIDIA Jetson platforms with JetPack 6.2. It provides a complete perception pipeline with real-time object detection, lane detection, traffic sign recognition, stereo depth estimation, and driver monitoring capabilities.

Bantay Drive - A mobile-based driver monitoring system built with Flutter and TFLite. It detects drowsiness and distraction in real time using a hybrid deep learning model, providing escalating alerts to improve road safety. [Note: The Bantay Drive application will be released soon.]

Driver Emotion and Fatigue Alert System (DEFAS) - A real-time driver monitoring solution combining 3D facial landmarks (MediaPipe) and MobileNetV3 (PyTorch) with PC and Flutter mobile applications.

🚨 Detect accidents in images using AI and initiate emergency responses for smarter city and traffic management.

Real-time driver yawn detection system using Mediapipe and YOLOv8 — an ADAS Driver Monitoring module that triggers alerts based on drowsiness level.

FaceMap AI is an intelligent computer vision application designed to enhance road safety by detecting driver drowsiness in real time. The system leverages MediaPipe Face Mesh and Deep Learning techniques to analyze facial landmarks from live video streams and identify signs of fatigue before they become dangerous.

A Python-based Automotive toolkit implementing A* pathfinding, the GOLD algorithm with YOLOv8 for Lane/Obstacle detection, and a MediaPipe/FastICA Driver Monitoring System for gaze tracking and rPPG heart rate estimation.

Real-Time Driver Monitoring System using OpenCV, MediaPipe, Drowsiness Detection, Yawning Detection, Gaze Tracking and Head Pose Estimation.

A real-time driver drowsiness detection system using computer vision to monitor eye movements. It alerts drivers with a buzzer when signs of fatigue are detected, helping prevent accidents.

A teen driver monitoring system built using Arduino Nano

Real-time driver fatigue detection using computer vision, explainable AI (SHAP), participant-aware evaluation and temporal decision intelligence.

🛡️ GuardianDrive AI: The Ultimate Driver Drowsiness Detection & Drunk Driver Alert System using Python, OpenCV & MediaPipe. Features Real-time Anti-Sleep Alarm, Intoxication Detection, IoT Alerts & Insurance Telematics. Production-ready for all face types. 🚀

driver monitoring system

A hybrid computer vision–based driver drowsiness detection system using MediaPipe Face Mesh and Dlib facial landmarks for real-time eye closure and yawn detection, featuring adaptive EAR calibration, audio alerts, SMS notifications, and image capture.

Occlusion-robust driver monitoring (DMS): ORFormer+HGNet landmark restoration + occlusion-aware dynamic fusion → action/gaze/hands/talk under facial occlusion & low-light IR. Sejong Univ. capstone (award-winning).

A monthly updated and 100% open-sourced fork of openpilot with clean commits dedicated to serve the openpilot community! FrogPilot is shaped by user and developer contributions, emphasizing collaborative, community-driven development to provide a bleeding-edge openpilot experience for everyone!