• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.213-224
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• 2009

This paper presents a new algorithm for estimating a driver's physical condition using real-time vision system and performs experimentation for real facial image data. The system relies on a face recognition to robustly track the center points and sizes of person's two pupils, and two side edge points of the mouth. The face recognition constitutes the color statistics by YUV color space together with geometrical model of a typical face. The system can classify the rotation in all viewing directions, to detect eye/mouth occlusion, eye blinking and eye closure, and to recover the three dimensional gaze of the eyes. These are utilized to determine the carelessness and drowsiness of the driver. Finally, experimental results have demonstrated the validity and the applicability of the proposed method for the estimation of a driver's physical condition.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.48-51
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• 1997

The purpose of this paper is to propose a method to protect the drowsiness of a driver. We measured the physiological signals, response time, and ace expression of the subjects in normal and drowsy state. Those data are used to establish the drowsiness index and fuzzy system. We employed the computer vision technology to extract and eye, track eyelids and measure the parameters related to drowsiness. These parameters were ed into the fuzzy system to decide the drowsiness level, When the drowsiness was detected, the fuzzy system generated warning signals which cons ist of sound and fragrance. Our system was available in decision of the drowsiness level and improvement of subjects' state.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.131-132
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• 1998

The purpose of this study is to keep drivers from falling asleep at the wheel, it is necessary to find ways of detecting and relieving drowsiness. For the estimation of our warning system, we measured the physiological parameters such as EEG, ECG, EOG while they performed a monotonous task intended to induce drowsiness. The effects of a oxygen, odor and various colors on the subjects while in a drowsy state were examined. It was found that a combination of a certain amount of oxygen and odor such as a menthol and yellow color can have a positive effect of relieving drowsiness.

• Annual Conference of KIPS
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• 2012.11a
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• pp.597-600
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• 2012

본 연구의 목적은 눈 폐쇄 상태 검출 알고리즘을 개발하고, 그것을 바탕으로 모바일 환경의 졸음운전 감지 시스템을 구현하는 것이다. 개발한 알고리즘은 검출된 눈 영역의 이미지를 히스토그램 분석을 통해 실험적으로 얻은 문턱 값으로 이진화 시킨 후 운전자 눈의 폐쇄 상태를 판단한다. 구현한 시스템은 얼굴과 눈 검출이 완료된 상태에서 검출된 눈이 폐쇄 상태인지를 판단한다. 폐쇄 상태인 경우 이상태가 지속되면 시스템은 운전자가 졸음운전 상태임을 감지하고 경고해준다. 자원이 제한된 모바일의 특성상 이미지 처리의 정확성뿐만 아니라 처리속도의 효율성도 중요한데 이 특성에 맞는 알고리즘을 개발하였고, 이를 바탕으로 졸음운전 감지 시스템 구현에 성공하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.962-970
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• 2021

The cause of the majority of vehicle accidents is a safety issue due to the driver's inattention, such as drowsy driving. A forward collision warning system (FCWS) can significantly reduce the number and severity of accidents by detecting the risk of collision with vehicles in front and providing an advanced warning signal to the driver. This paper describes a low power embedded system based FCWS for safety. The algorithm computes time to collision (TTC) through detection, tracking, distance calculation for the vehicle ahead and current vehicle speed information with a single camera. Additionally, in order to operate in real time even in a low-performance embedded system, an optimization technique in the program with high and low levels will be introduced. The system has been tested through the driving video of the vehicle in the embedded system. As a result of using the optimization technique, the execution time was about 170 times faster than that when using the previous non-optimized process.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.2
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• pp.42-54
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• 2011

This study presented an algorithm to detect zigzag driving maneuver that is highly associated with vehicle crash occurrence. In general, the zigzag driving results from the driver's inattention including drowsy driving and driving while intoxicated. Therefore, the technology to detect such unsafe driving maneuver will provide us with a valuable opportunity to prevent crash in the road. The proposed detection algorithm used angular velocity data obtained from a gyro sensor. Performance evaluations of the algorithm presented promising results for the actual implementation in practice. The outcome of this study can be used as novel information contents under the ubiquitous transportation systems environment.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.491-498
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• 1997

This paper describes the arousal measurement and control system using fuzzy logic to prevent drowsy driving. Sugeno's method was used for fuzzy inference in this study. Arousal evaluation and control criteria were modified from result of Nz-IRI analysis depending on arousal sate. Membership function and rule base of fuzzy inference were determined from the modified arousal level criteria When lRl (Inter-SIR Interval) was shorter than 60sec, outputs of both methods were changed from small to big, but output of three step warning method was same level until the next warning range. Since output of fuzzy inference tracked well the change of subject's arousal level, problems of three step warning method could be overcome by fuzzy inference method Furthermore, the output of the fuzzy inference was highly correlated with Nz(r = 0.99). Therefore, the fuzzy inference method for evaluation and the control of arousal will be more effective at real driving situation than three step warning method.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.67-77
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• 2016

In this paper, we propose an algorithm for open/closed eye detection based on modified Hausdorff distance. The proposed algorithm consists of two parts, face detection and open/closed eye detection parts. To detect faces in an image, MCT (Modified Census Transform) is employed based on characteristics of the local structure which uses relative pixel values in the area with fixed size. Then, the coordinates of eyes are found and open/closed eyes are detected using MHD (Modified Hausdorff Distance) in the detected face region. Firstly, face detection process creates an MCT image in terms of various face images and extract criteria features by PCA(Principle Component Analysis) on offline. After extraction of criteria features, it detects a face region via the process which compares features newly extracted from the input face image and criteria features by using Euclidean distance. Afterward, the process finds out the coordinates of eyes and detects open/closed eye using template matching based on MHD in each eye region. In performance evaluation, the proposed algorithm achieved 94.04% accuracy in average for open/closed eye detection in terms of test video sequences of gray scale with 30FPS/$320{\times}180$ resolution.

• 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 Digital Convergence
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• v.14 no.11
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• pp.305-311
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• 2016

Even though automakers have actively been conducting studies on autonomous navigation thanks to the development and application of wireless Internet technology, the traffic accident has been kept unsolved. The causes of the accident are drowsy driving, a mistake of a driver, environmental factors, and a wrong road structure; Driving manner and characteristics of a driver among the causes are significantly influential for the accident. In this paper, a study to measure characteristics of zigzag driving that can be seen before an occurrence of an accident regarding traffic accidents that can be incurred while driving manually or autonomously was conducted. While existing studies measured zigzag driving based on characteristics of the change of lateral angular velocity by imaging techniques or driving manner on the first and second lane, this study proceeded to measure zigzag driving by setting a lateral moving distance and a critical value range by utilizing the value of a sensor.