AI-Driven Advancement for Driver Drowsiness Detection using Behaviour Analysis
Keywords:
Driver Drowsiness Detection, CPU, OpenCV, Machine Learning, Real-time Monitoring, Webcam-based SystemAbstract
Driver fatigue is a leading contributor to road accidents worldwide, particularly during long-distance or night-time travel. This project presents a real-time Driver Drowsiness Monitoring System that leverages visual behavior metrics and rule-based thresholds to detect early signs of sleepiness. A simple Tkinter GUI facilitates one-click activation of webcam monitoring, while OpenCV captures and preprocesses video frames.
References
W. L. Ou, M. H. Shih, C. W. Chang, X. H. Yu, C. P. Fan, "Intelligent Video-Based Drowsy Driver Detection System under Various Illuminations and Embedded Software Implementation", 2015 international Conf. on Consumer Electronics - Taiwan, 2015.
W. B. Horng, C. Y. Chen, Y. Chang, C. H. Fan, “Driver Fatigue Detection based on Eye Tracking and Dynamic Template Matching”, IEEE International Conference on Networking,, Sensing and Control, Taipei, Taiwan, March 21-23, 2004.
