Journal of Information Technology & Software Engineering
Open Access
ISSN: 2165- 7866
ISSN: 2165- 7866
S. Barathvaj*, B. Ashwanth, M. Shobana, L. Jabasheela
Driver drowsiness is a critical cause of road accidents, especially when drivers travel long distances at night. The ongoing, real-time drowsiness detection system makes use of computer vision and machine learning algorithms to measure facial features, especially eye movements, for enhanced road safety. It calculates the Eye Aspect Ratio (EAR) by scanning dlib-detected facial points and processes video using OpenCV. The steady decrease in EAR shows that the person is sleepy. Therefore, pyttsx3 will provide an audio warning to the driver without diverting their attention. It is flexible and accessible and thus possibly used both as a configurable in-vehicle solution and also as an application for different types of smartphones. With it, real-time monitoring is possible and drowsiness detection happens immediately, reducing greatly the possibility of accidents due to drowsiness. This is the cost-effective approach to observe eye movements as an indicator of alertness.
Published Date: 2025-02-28; Received Date: 2024-10-16