Year: 2025
Type: article
Abstract: In this research paper, Driver drowsiness is a major cause of road accidents worldwide, resulting in severe injuries and loss of life. A real-time drowsiness detection system aims to enhance road safety by continuously monitoring the driver's alertness and providing timely warnings to prevent accidents. This system typically uses computer vision and machine learning techniques to analyze facial landmarks such as eye closure, blink rate, and head position. By integrating a camera with a microcontroller or computing device, the system captures live video feeds, processes them in real-time, and detects signs of fatigue. When drowsiness is detected, an alert is triggered through an audio or visual signal. This proactive approach provides an effective solution for mitigating fatigue- related accidents, especially in long-distance or night driving scenarios. Key Words: Drowsiness detection, Real-Time Monitoring, Driver Safety, Computer Vision, Eye Aspect Ratio (EAR), Fatigue Detection, Machine Learning, Facial Landmark Detection, Blink Detection, Road Safety
Authors: Ms. Roopa, Ashwini R
Institution: JSS Academy of Higher Education and Research
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4410380010