Background: Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver.
Methods: The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in real-time using Raspberry Pi. Video samples were processed using the Haar Cascade Classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected.
Results: Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88.00%, while the highest accuracy value for drowsiness driver detection was 90.40%.
Conclusion: In general, a system developed with image processing methods has been able to monitor the drowsiness and lost focused drivers with high accuracy. This system still needs improvements to increase performance.
Keywords: Drowsiness detection; Haar cascade classifier; Lost focused driver; Raspberry Pi; Real-time.
Copyright © 2020 Adi et al. Published by Tehran University of Medical Sciences.