Manuscript Number : SHISRRJ247240
Driver Drowsiness Detection Using Machine Learning
Authors(2) :-Dr. K. Shanmugam, C. Bhavya Sree The Drowsiness Detection System presented in this project addresses the critical issue of drowsy behavior during activities demanding sustained attention, such as driving. Leveraging computer vision techniques and libraries like Dlib and OpenCV, the system provides real-time monitoring of facial features, with a specific focus on the eyes. The calculation of the Eye Aspect Ratio (EAR) serves as a key metric for assessing drowsiness, enabling the system to trigger timely alerts when signs of drowsiness are detected. Configurable parameters, including EAR thresholds and frame check values, allow users to tailor the system to individual preferences. The integration of visual and auditory alert mechanisms enhances user awareness, contributing to accident prevention and promoting responsible behavior. The project report details the methodology, implementation specifics, and results, highlighting the system's potential applications in automotive safety, transportation, healthcare, education, and beyond. The Drowsiness Detection System represents a practical and effective solution for enhancing safety in scenarios where sustained attention is paramount
Dr. K. Shanmugam Drowsiness detection, Fatigue detection, Driver fatigue monitoring, Sleepiness detection, Alertness monitoring, Eye tracking Facial recognition, Driver assistance systems, Driver alertness monitoring. Publication Details Published in : Volume 7 | Issue 2 | March-April 2024 Article Preview
Assistant Professor, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India
C. Bhavya Sree
Post Graduate, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India
Date of Publication : 2024-04-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 147-153
Manuscript Number : SHISRRJ247240
Publisher : Shauryam Research Institute
URL : https://shisrrj.com/SHISRRJ247240