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All Journal Data Science: Journal of Computing and Applied Informatics
Norazlin Ibrahim
norazlin@unikl.edu.my
Computer Science & IT

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Journal : Data Science: Journal of Computing and Applied Informatics

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Classification for Driver’s Distraction and Drowsiness Through Eye Closeness Using Receiver Operating Curve (ROC) Anis Hazirah Rodzi; Zalhan Bin Mohd Zin; Norazlin Ibrahim
Data Science: Journal of Computing and Applied Informatics Vol. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1540.308 KB) | DOI: 10.32734/jocai.v4.i1-3516

Abstract

In Malaysia, driver inattention and drowsiness becomes one of the causes of road accidents which sometime could lead to fatal ones. From the data provided by Malaysian Police Force, Polis Di Raja Malaysia or PDRM in 2016, deaths from road accidents increased from 6,706 in 2015 to 7,512 in 2016. Some accidents were caused by human factor such as driver's inattention and drowsiness. This problem motivates many parties to look for better solution in dealing with this human factor. Some of the car manufacturers have introduced to their certain models of car with an assistant system to oversee driver’s condition. The assistant system is in fact part of the main safety system known as Advanced Driver Assistance Systems (ADAS). The kind of system has been developed to strengthen vehicle systems for safety and conducive driving. The system has been contemplated to congregate accurate input, rapid processing data, precisely predict context, and respond in real time. In addition to that, suitable method is also needed to detect and classify driver drowsiness and inattention using computer vision as the latter become more and more important in any intelligent system development. In this paper, the proposed system introduces a method to classify drowsiness into three different classes of eye state; open, semi close and close. The classification has been done by using feature extraction method, percentage of eye closure (PERCLOS) technique and Support Vector Machine (SVM) classifier. The performances of the methods have been then measured and represented by using confusion matrix and ROC performance graph. The results have show that the proposed system has been able to achieve high performance of distraction and drowsiness detection according to driver's eye closeness level.
Co-Authors Anis Hazirah Rodzi Zalhan Bin Mohd Zin