Development of an intelligent drowsiness detection system for drivers using image processing technique

• Main Authors: Suhaiman, A.A., May, Z., Rahman, N.A.A.
• Format: Conference or Workshop Item
• Published: Institute of Electrical and Electronics Engineers Inc. 2020
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097788418&doi=10.1109%2fSCOReD50371.2020.9250948&partnerID=40&md5=1f913578340b2c506ff63cb7362817c3; http://eprints.utp.edu.my/29960/

Drowsy driving highly contributes to a number of road accidents throughout the years. Car crashes or any unwanted incidents can be avoided by implementing a system with alarm output to alert drowsy drivers to focus on the road. An intelligent system is developed to detect driver drowsiness and trigger alarm to alert drivers as one way to prevent accidents, save money and reduce losses and sufferings. However, due to high variability of surrounding parameters, current techniques have several limitations. Bad lightings may affect camera ability to accurately measure the face and the eye of the driver. This will affect the analysis using image processing technique due to late detection or no detection hence decrease the accuracy and efficiency of the technique. Several techniques have been studied and analyzed to conclude the best technique with the highest accuracy to detect driver drowsiness. In this work, a real-time system that utilizes computerized camera to automatically track and process driver's eye using Python, dlib and OpenCV is proposed. The eye region of the driver is measured and calculated continuously to determine the drowsiness of the driver before triggering an output alarm to alert the driver. © 2020 IEEE.