Driver monitoring system
Driver Monitoring System (DMS) — Component Comparison
| Component | Purpose | Common Approaches / Models | Key Papers / References |
|---|---|---|---|
| Face Detection | Detect driver face | Haar Cascade, HOG+SVM, MTCNN, RetinaFace, YOLO | RetinaFace (2020) |
| Facial Landmark Detection | Detect key points on face | dlib 68-point, MediaPipe FaceMesh, FAN, 3DMM | MediaPipe FaceMesh (2020), FAN (2017) |
| Head Pose Estimation | Estimate yaw, pitch, roll | PnP, 3D Morphable Models, Hopenet, FSA-Net | Hopenet (2018), FSA-Net (2019) |
| Eye Tracking & Blink Detection | Detect eye closure / drowsiness | EAR (Eye Aspect Ratio), Iris tracking, CNN | EAR / PERCLOS (2000s) |
| Gaze Estimation | Predict driver gaze direction | Gaze360, ETH-XGaze, OpenGaze, MPIIGaze | Gaze360 (2019), MPIIGaze (2015) |
| Drowsiness / Distraction | Detect fatigue or inattentiveness | Blink rate, yawn detection, head nodding | DeepVAD (2021) |
| End-to-End Driver State | Combine multiple signals for prediction | CNN / RNN pipelines, multi-modal fusion | OpenDriver (2022) |
👁️ Driver Attention Monitoring
This video explains how modern Driver Monitoring Systems use cameras and machine learning to track eye gaze, head position, and attention state to improve road safety. It demonstrates real-time analysis of driver behavior, including distraction and alertness.