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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.

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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.

Keywords:
DistractionDrowsyFace/eye detectionFatigueOpenCVScleral area

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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.