• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.508-510
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• 2020

2015년도부터 2019년도까지 5년간 고속도로에서 1,079명의 사망자가 발생하였으며, 이중 졸음운전 및 주시 태만이 729명(67.6%)로 가장 많았다. 졸음운전 방지를 위해 휴게소, 졸음쉼터 등 노력하고 있으나 이러한 노력에도 졸음운전으로 인한 사고는 지금까지도 계속해서 발생하고 있다. 본 연구는 이러한 사고를 방지하기 위해 적외선 카메라를 이용한 영상 촬영하여 안면검출 해석과 서비스를 구현하였다. 안면검출을 통한 동공 상태의 여부와 적합한 수면 판단 기준으로 PERCLOS(Percentage of Eye Closure)을 적용하였다. 운전자의 동공의 장축과 단축의 비율이 1 : 0.35 미만 일 때, 운전자가 졸음상태라 판단하고 음성 알람을 통해 졸음방지를 개선할 수 있었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.2
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• pp.94-102
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• 2011

In this paper, a novel drowsiness detection algorithm using eye-blink pattern is proposed. The proposed drowsiness detection model using finite automata makes it easy to detect eye-blink, drowsiness and sleep by checking the number of input symbols standing for closed eye state only. Also it increases the accuracy by taking vertical projection histogram after locating the eye region using the feature of horizontal projection histogram, and minimizes the external effects such as eyebrows or black-framed glasses. Experimental results in eye-blinks detection using the JZU eye-blink database show that our approach achieves more than 93% precision and high performance.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1393-1394
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• 2015

본 논문은 비접촉식 센서를 통한 심전도 신호를 이용하여 운전자의 졸음을 검출하는 시스템을 제안한다. 이를 위해 비접촉식으로 얻은 심전도 신호에 적합한 특성들을 추출하였으며 t-test를 이용하여 검출 시스템에서 사용할 특성을 선택하였다. 그 후 추출된 특성으로 구성된 데이터를 support vector machine을 활용하여 학습함으로써 졸음 검출 시스템을 구축하였다. 10명의 실험자를 대상으로 한 실험 결과, 각 실험자에 대해서는 평균 91.25 %, 모든 실험자를 대상으로 한 실험에서는 81.29 %의 정확도로 졸음을 검출하는 것이 가능하였다.

• Proceedings of the Korea Information Processing Society Conference
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.249-252
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• 2005

본 논문에서는 자동차를 운전 시에 운전자의 얼굴과 눈의 영역을 자동으로 검출하고 눈동자를 추적하여 운전자의 졸음 여부를 판단하는 효과적인 시스템 구현방법을 제안한다. 복잡한 배경에서 얼굴과 눈을 검출하는 방법은 Haar-like feature의 원리를 이용하고 졸음운전으로 판단하는 방법은 눈동자 영역의 특성과 눈동자의 검출 유무, 움직임 등의 인식을 통하여 졸음운전 경보시스템의 실용화에 대한 가능성을 확인한다.

• Journal of Convergence for Information Technology
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• v.8 no.3
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• pp.193-198
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• 2018

Recently, the casualties of automobile traffic accidents are rapidly increasing, and serious accidents involving serious injury and death are increasing more than those of ordinary people. More than 70% of major accidents occur in drowsy driving. Therefore, in this paper, we studied the drowsiness prevention system to prevent large-scale disasters of traffic accidents. In this paper, we propose a real-time flicker recognition method for drowsy driving detection system and drowsy recognition according to the increase of carbon dioxide. The drowsy driving detection system applied the existing image detection and the deep running, and the carbon dioxide detection was developed based on the IoT. The drowsy prevention system using both of these techniques improved the accuracy compared to the existing products.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.5
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• pp.564-570
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• 2014

This paper propose a method to detect and track a human face using depth information as well as color images for detection of drowsy driving. It consists of a face detection procedure and a face tracking procedure. The face detection procedure basically uses the Adaboost method which shows the best performance so far. But it restricts the area to be searched as the region where the face is highly possible to exist. The face detected in the detection procedure is used as the template to start the face tracking procedure. The experimental results showed that the proposed detection method takes only about 23 % of the execution time of the existing method. In all the cases except a special one, the tracking error ratio is as low as about 1 %.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.947-950
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• 2015

In the cause of car accidents in Korea, drowsy driving has shown that it is larger fctors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. Furthermore, Many researches have been published that measuring alpha wave of EEG signals is the effective way in order to be aware of drowsiness of drivers. In this study, we have developed EEG measuring device that applies a signal processing algorithm using the LabView program for detecting drowsiness. According to results of drowsiness inducement experiments for small test subjects, it was able to detect the pattern of EEG, which means drowsy state based on the changing of power spectrum, counterpart of alpha wave. After all, Comparing to the results of drowsiness pattern between commercial equipments and developed device, we could confirm acquiring similar pattern to drowsiness pattern. With this results, the driver's drowsiness prevention system expect that it will be able to contribute to lowering the death rate caused by drowsy driving accidents.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.30-39
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• 2012

In this paper, a safe driving assistance system is proposed by detecting the status of driver's doze based on face and eye detection. By the level of the fatigue, safe driving system alarms or set the seatbelt on vibration. To reduce the effect of backward light and too strong solar light which cause a decrease of face and eye detection rate and false fatigue detection, post processing techniques like image equalization are used. Haar transform and PCA are used for face detection. By using the statistic of the face and eye structural ratio of normal Koreans, we can reduce the eye candidate area in the face, which results in reduction of the computational load. We also propose a new eye status detection algorithm based on Hough transform and eye width-height ratio, which are used to detect eye's blinking status which decides doze level by measuring the blinking period. The system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. In this paper, four algorithms are implemented and proposed algorithm is made based on the probability model and we achieves 84.88% of correct detection rate through indoor and in-car environment experiments. And also we achieves 69.81% of detection rate which is better result than that of other algorithms using IR camera.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.464-470
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• 2016

In this paper, we propose an improved face detection algorithm and determination method for drowsiness status of driver from the opening and closing frequency of the detected eye. For this purpose, face, eyes, nose, and mouth are detected based on conventional Viola-Jones face detection algorithm and spatial correlation of face. Here the spatial correlation of face is performed by DFP(Detect Face Part) based on seven characteristics. The experimental results on Caltect face image database revealed that the detection rates of noise particularly showed the improved performance of 13.78% in comparison to that of the previous Viola-Jones algorithm. Furthermore, we analyze the driver's drowsiness determination cumulative value of the eye closed state as a function of time based on SVM (Support Vector Machine) and PERCLOS(Percentage Closure of Eyes). The experimental results confirmed the usefulness of the proposed method by obtaining a driver's drowsiness determination rate of 93.28%.