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Drowsy driver mobile application: Development of a novel scleral-area detection method.
Faisal Mohammad1, Kausalendra Mahadas1, George K Hung1
1Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA.
Computers in Biology and Medicine
|August 9, 2017
Summary
A new mobile app detects driver drowsiness using face and eye detection. It alerts drivers when a low count of white pixels in the eye region indicates fatigue.
Area of Science:
- Computer Vision
- Human-Computer Interaction
- Biomedical Engineering
Background:
- Driver drowsiness is a significant cause of road accidents.
- Existing methods for drowsiness detection can be intrusive or unreliable.
- There is a need for a practical, real-time driver monitoring system.
Purpose of the Study:
- To develop a reliable and practical mobile application for detecting driver drowsiness.
- To implement a system capable of real-time monitoring and alerting drivers.
Main Methods:
- Utilized a Haar cascade classifier (OpenCV) for face and eye detection.
- Developed JAVA software for image processing on a masked eye region.
- Quantified scleral white pixels to determine eye-opening state.
Main Results:
- The system successfully detected drowsiness by analyzing white pixel counts in the sclera.
- The drowsiness detection app was validated on static images, laboratory subjects, and in-vehicle conditions.
- A low white-pixel count reliably indicated drowsiness, triggering an alarm.
Conclusions:
- The developed mobile app provides a reliable and practical solution for driver drowsiness detection.
- The system's successful implementation in various environments demonstrates its potential for enhancing road safety.
- Real-time monitoring and alerts can mitigate risks associated with driver fatigue.
