Drowsiness Detection Using Machine Learning
Ashish Chauhan , Shivam Mishra, Ayam Sharma, Ananya M Bhat
Drowsiness, Eye aspect ratio (EAR), Mouth aspect ratio (MAR), OpenCV, Eye-blinked detection, Dlib
The number of road accidents are increasing rapidly over the years. Therefore, driver drowsiness and fatigue detection are major possible area to prevent a large number of sleeps induced road accidents. Several techniques have been studied and analysed to conclude the best technique with the highest accuracy to detect driver drowsiness. Nowadays, there are many theories about driving drowsiness detection technique. However, most of the work is finding the general solution for all drivers. The paper is proposing a real-time driving drowsiness detection algorithm that is driver specific. The work shows real-time system that utilizes camera to automatically track and process driver’s eye using Python, dlib and OpenCV. The eye region and the mouth region of the driver is measured continuously to check the drowsiness of the driver. According to the face’s landmarks, new parameters, called Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR), is introduced to evaluate the drowsiness of driver in the current frame. Through experiments, we demonstrate this algorithm with respect to current driving drowsiness detection approaches in both accuracy and speed. we show this algorithm with respect to current system in terms of speed and accuracy.
Article Details
Unique Paper ID: 155981

Publication Volume & Issue: Volume 9, Issue 2

Page(s): 655 - 659
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