Classification for Driver’s Distraction and Drowsiness Through Eye Closeness Using Receiver Operating Curve (ROC)

Data Science: Journal of Computing and Applied Informatics

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Field Value
 
Title Classification for Driver’s Distraction and Drowsiness Through Eye Closeness Using Receiver Operating Curve (ROC)
 
Creator Anis Hazirah Rodzi
Zin, Zalhan Bin Mohd
Norazlin Ibrahim
 
Subject Classification
Distraction
Drowsiness
Driver
Advanced Driver Assistance Systems
Receiver Operating Curve
PERCLOS
SVM
 
Description 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.
 
Publisher Talenta Publisher
 
Date 2020-02-05
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://talenta.usu.ac.id/JoCAI/article/view/3516
10.32734/jocai.v4.i1-3516
 
Source Data Science: Journal of Computing and Applied Informatics; Vol. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI); 15-26
2580-829X
2580-6769
 
Language eng
 
Relation https://talenta.usu.ac.id/JoCAI/article/view/3516/2719
 
Rights Copyright (c) 2020 Data Science: Journal of Computing and Applied Informatics
https://creativecommons.org/licenses/by-nc-nd/4.0
 

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