• International Journal of Highway Engineering
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• v.10 no.4
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• pp.127-138
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• 2008

Vehicles have recently become one of the main factors affecting our quality of life, and the needs of vehicles are still increasing. As a result, the growth of vehicles generate more crashes every year. One main factor for vehicle crashes is uncareful driving behaviors. Especially, drowsy or fatigue driving behaviors explain about 10-20% of the crashes, and they cause serious results because of the delay of response time and the decrease of object-recognition. Therefore, this research conducted real time image processing tests in order to study how cellular phone usages and drowy(or fatigue) drives affect driving behaviors. A vehicle simulator was used for this research, and the faceLAB 4.5 of Seeing Machines for eye image tracking tests using a small camera was installed in the front of the simulator, and normal and drowsy(or fatigue) driving patterns were analyzed.

• 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%.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.719-722
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• 2016

운전자의 주의력 감쇠는 교통사고 요인에 있어서 큰 비중을 차지한다. 주의력 감쇠는 무선 통화, 기기 조작, 졸음으로 나타날 수 있는데 자동차 대형사고의 대부분은 졸음운전으로 인하여 일어나며, 졸음운전 시에는 운전자의 운전조작 및 방어 조작 능력이 현저하게 저하한다. 본 시스템은 카메라로부터 실시간으로 영상 데이터를 입력 받아 처리하여 운전자의 졸음 상태를 인식하는 시스템으로 운전자에게 졸음방지 기능을 제공한다. Haar-Like Feature cascade classifier 방법을 사용하여 얼굴 및 눈 영역 검출을 하였고 Open Eye, Closed Eye가 학습된 MLP(Multi-Layer Perceptron)를 이용해 눈 깜박임을 인식하여 PERCLOS(Percentage of Eye Close)방법으로 졸음을 판단하였다. 본 논문에서 제안한 방법의 인식률의 정확도를 검증하기 위해 인식률 테스트를 하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.519-520
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• 2021

본 논문에서는 딥러닝 기반의 얼굴인식과 Harr Cascade 분류기를 이용한 눈인식, 스마트워치를 매개로 한 심박수 측정을 활용하여 운전자 졸음운전 경고 시스템을 제안하였다. 제안하는 시스템은 PERCLOS 방법을 적용하여 운전자의 눈 감은 시간을 누적시켜 졸음 상태 유무를 판단하고, 스마트워치의 HR센서를 활용한 운전자의 심박수 값 모니터링을 진행하여 졸음 발생 시 경고음을 발생시켜 졸음운전으로 인한 교통사고를 예방할 수 있다.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.259-260
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• 2024

졸음운전으로 인한 교통사고는 매년 꾸준하게 일어나 이에 대한 다방면의 해결책이 요구되고 있다. 본 논문에서는 위 문제를 개선하고자 ChatGPT와 영상처리를 이용한 졸음 감지 시스템을 구현하였다. 이 시스템은 운전자의 얼굴 부분을 영상처리로 인식하여 눈동자의 종횡비를 구해 PERCLOS 공식에 따른 운전자의 졸음을 판별시키고, 경고와 동시에 ChatGPT가 운전자에게 특정 주제를 키워드로 TTS와 STT를 통해 대화한다. 운전자의 졸음을 판별하기 위해 임베디드 보드에서 연결된 캠을 통해 졸음 판별을 하고, ChatGPT도 마찬가지로 보드에서 연결한 스피커, 마이크를 통해 운전자와 대화한다. 이를 활용하여 운전자의 졸음 자각을 통한 안전운전 및 사고 발생률의 감소를 기대할 수 있다.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.255-265
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• 2007

In this paper, we propose a new illumination-robust drowsy driver monitoring system with single CCD(Charge Coupled Device) camera for intelligent vehicle in the day and night. For this system that is monitoring driver's eyes during a driving, the eye detection and the measure of eyelid movement are the important preprocesses. Therefore, we propose efficient illumination compensation algorithm to improve the performance of eye detection and also eyelid movement measuring method for efficient drowsy detection in various illumination. For real-time application, Cascaded SVM (Cascaded Support Vector Machine) is applied as an efficient eye verification method in this system. Furthermore, in order to estimate the performance of the proposed algorithm, we collect video data about drivers under various illuminations in the day and night. Finally, we acquired average eye detection rate of over 98% about these own data, and PERCLOS(The percentage of eye-closed time during a period) are represented as drowsy detection results of the proposed system for the collected video data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.887-898
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• 2005

In this paper, we introduce a probabilistic model based on Bayesian networks BNs) for recognizing human fatigue. First of all, we measured face feature information such as eyelid movement, gaze, head movement, and facial expression by IR illumination. But, an individual face feature information does not provide enough information to determine human fatigue. Therefore in this paper, a Bayesian network model was constructed to fuse as many as possible fatigue cause parameters and face feature information for probabilistic inferring human fatigue. The MSBNX simulation result ending a 0.95 BN fatigue index threshold. As a result of the experiment, when comparisons are inferred BN fatigue index and the TOVA response time, there is a mutual correlation and from this information we can conclude that this method is very effective at recognizing a human fatigue.

• Journal of the Ergonomics Society of Korea
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• v.25 no.4
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• pp.129-135
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• 2006

he purpose of this study is to measure bio-signal to investigate the driver's physiological response change under real situation using train simulator. The train simulator used in this study is KTX model and according to changes of driving situation, The bio-signal controlled by autonomic nervous system, such as GSR(Galvanic Skin Response), SpO2(Saturation percent O2), HR(Heart Rate), ECG(Electrocardiograph), EEG(Electroencephagram) and movement and response of eye were measured. Statistically significant difference in bio-signal data and eye movement activity pattern were investigated under several different driving speeds using analysis of variance (p<0.05). The GSR and HR value measured in average and mission speed operation is higher than in high-speed operation. β wave of EEG in average speed operation become more activated than in high speed operation. In accordance with a characteristic of rail vehicle, movement and response of eye in high-speed operation requiring relatively simple maneuver become less activated than in either average or mission speed operations. Conclusively, due to more careful driving controls in average and mission speed operation are required than in high-speed operation, level of mental and physical stresses of train driver was increased and observed through changes of bio-signal and eye movement measured in this study.