• Journal of Multimedia Information System
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• v.5 no.3
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• pp.189-194
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• 2018

In this paper, we propose the drowsiness detection method for car driver. This paper determines whether or not the driver's eyes are closed using the depth and infrared videos. The proposed method has the advantage to detect drowsiness without being affected by illumination. The proposed method detects a face in the depth picture by using the fact that the nose is closest to the camera. The driver's eyes are detected by using the extraction of harr-like feature within the detected face region. This method considers to be drowsiness if eyes are closed for a certain period of time. Simulation results show the drowsiness detection performance for the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.153-154
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• 2019

There are many jobs, not actually sleeping. Sleepy driving is one of the biggest problems in modern society. In this paper, we propose a system to control underwater guns by using deep learning (YOLO) to check eyes and to check drowsiness. So let your mind be clear.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of Sensor Science and Technology
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• v.23 no.2
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• pp.142-147
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• 2014

Real time driver's respiration monitoring method for detecting driver's drowsiness is investigated. The sensor to obtain driver's respiration signal was a piezoelectric pressure sensor attached at the abdominal region of the seat belt. The resistance of the pressure sensor was changed according to the pressure applied to the seat belt due to the driver's respiration. Monitoring driver's respiration was carried out by driving on the virtual road in a driving simulator from Cheonan to Seoul and monitoring results were compared to the PELCLOS. Experiment results show that the driver's respiration signal can be used for detecting driver's drowsiness.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.432-435
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• 2007

In modern society, automobile is necessary vehicle. Automobile is a series of modern convenient. And the number of automobile is on the increase. But from you to year, automobile accident is on the increase, also. One of most reason that cause automobile accident is drowsiness driving. So, we plan to construct automobile system for drowsiness accident prevention for decreasing automobile accident. In this research, we built a drowsiness detecting device using EDA (Electro Dermal Activity) signal which is electric response in the human body skin. There are SIL (Skin Impedance Level) and SIR (Skin Impedance Response) in EDA. Mostly, SIL consists of DC element. On the other hand, SIL consists of AC element. We could detect the drowsiness condition with SIL and SIR signal.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2281-2284
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• 2008

The purpose of this paper is to develope the drowsiness-drive perception system which judges drowsiness driving based on drivers' eye region using single vision system. To do this, first, we use the Haar-like feature and AdaBoost learning algorithm for detecting the features of the face region. And we measure the eye blinking frequency and eye closure duration from these feature data. And then, we propose the drowsiness-drive detection algorithm using the eye blinking frequency and eye closure duration. Finally, we have shown the effectiveness and feasibility of the proposed method through some experiments.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.166-172
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• 2018

The distraction of the driver's attention causes as much traffic accidents as drowsiness driving. Yet though there have been many studies on drowsiness driving, research on distraction driving is insufficient. In this paper, we divide distraction of attention into visual distraction and cognitive distraction and analyze the EEG of subjects while viewing images of distracting situations. The results show that more information is received and processed when distractions occur. It is confirmed that the probability of accident increases when the driver receives overwhelming amount of information that he or she cannot concentrate on driving.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1852-1861
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• 2016

Drowsy driving is a significant factor in traffic accidents, so driver drowsiness detection system based on computer vision for convenience and safety has been actively studied. However, it is difficult to accurately detect the driver drowsiness in complex background and environmental change. In this paper, it proposed the driver drowsiness detection algorithm to determine whether the driver is drowsy through the measurement standard of a yawn, eyes drowsy status, and nod based on facial features. The proposed algorithm detect the driver drowsiness in the complex background, and it is robust to changes in the environment. The algorithm can be applied in real time because of the processing speed faster. Throughout the experiment, we confirmed that the algorithm reliably detected driver drowsiness. The processing speed of the proposed algorithm is about 0.084ms. Also, the proposed algorithm can achieve an average detection rate of 98.48% and 97.37% for a yawn, drowsy eyes, and nod in the daytime and nighttime.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.859-864
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• 2022

Drowsy driving, one of the biggest causes of traffic accidents every year, is accompanied by various factors. As a general method to check whether or not there is drowsiness, a method of identifying a driver's expression and driving pattern, and a method of analyzing bio-signals are being studied. This paper proposes a driver fatigue detection system using deep learning technology and bio-signal measurement technology. As the first step in the proposed method, deep learning is used to detect the driver's eye shape, yawning presence, and body movement to detect drowsiness. In the second stage, it was designed to increase the accuracy of the system by identifying the driver's fatigue state using the pulse wave signal and body temperature. As a result of the experiment, it was possible to reliably determine the driver's drowsiness and fatigue in real-time images.