Paper Title
Driver Distraction Detection
Article Identifiers
Registration ID: IJNRD_222365
Published ID: IJNRD2407002
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Authors
Keywords
Dlib,EAR, Gaze score, MediaPipe
Abstract
Driving safety is a paramount concern in today's world, with road accidents claiming thousands of lives every year. Among the various factors contributing to these accidents, driver fatigue, distraction, and drowsiness stand out as significant threats to road safety. To address these challenges, this project presents a novel approach to real-time driver state detection using advanced computer vision techniques. Leveraging the capabilities of the MediaPipe library, the system analyzes 68 facial keypoints to compute essential metrics such as Eye Aspect Ratio (EAR), Gaze Score, and Head Pose. These metrics serve as indicators of the driver's state, allowing for the classification of normal, tired, drowsy, distracted, or potentially asleep conditions. By continuously monitoring these parameters, the system alerts the driver to take necessary actions, thereby enhancing road safety and reducing the risk of accidents.
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How To Cite (APA)
Sneha R, Swahi J, Karthika S, Manju Priya S, & Gokul Sri S (July-2024). Driver Distraction Detection. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), a6-a11. https://ijnrd.org/papers/IJNRD2407002.pdf
Issue
Volume 9 Issue 7, July-2024
Pages : a6-a11
Other Publication Details
Paper Reg. ID: IJNRD_222365
Published Paper Id: IJNRD2407002
Research Area: Engineering
Author Type: Indian Author
Country: Coimbatore, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2407002.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407002
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