• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.132-133
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• 2015

본 논문에서는 블랙박스 카메라에 적합한 기하왜곡 보정 기법을 제안한다. 제안하는 기법은 정교한 카메라 보정 기법 없이 카메라 중심점을 지나는 직선주행 평균 영상에 나타나는 소실점과 카메라 중심점의 기하학적 관계와 기하왜곡에 의해 왜곡된 소실선을 특징으로 한 향상된 Hough 기법을 이용하여 기하왜곡된 블랙박스 카메라 영상에 대한 단일 기하왜곡 매개변수 모델을 추정한다. 제안하는 기법은 각각의 기하왜곡 매개변수 후보들에 의해 보정된 영상의 소실선들에 대한 에지픽셀들의 향상된 Hough 기법의 투표과정을 통한 최적의 후보선출 과정, 선출된 기하왜곡 매개변수를 초기변수로 최다 투표된 소실선들에 대한 에너지함수 최적화 과정, 최적의 기하왜곡 매개변수를 추정하여 왜곡된 블랙박스 카메라 영상보정 과정으로 이뤄진다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.438-441
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• 2013

알려진바 한 해 우리나라에서 200여 건 이상의 차량 급발진으로 추청 되는 사고(Sudden Unintended Acceleration, SUA)가 발생하지만 지금까지 명확하게 급발진으로 추정되는 사고의 원인을 밝혀내지 못해 사고를 당한 운전자는 막대한 정신적, 물질적 피해를 보고 있다. 이에 차량의 급발진으로 추정되는 사고 시 차량의 상태 및 원인 파악을 위하여 차량 내 설치되는 OBD-II(On Board Diagnostics-II)와 운전자의 발쪽을 촬영하는 Foot Camera를 이용하여 통합 블랙박스 시스템을 구현하고자 한다. 지금까지의 블랙박스 시스템은 단순히 사고 시 영상을 촬영 저장하여 운전자에게 보여 주는 장치라면 현재 구현하려는 통합시스템은 블랙박스 자체에서 이동 통신을 통한 차량데이터 실시간 서버 전송 및 스마트 폰과 스마트 패드의 앱(App)으로 OBD-II와 메인서버와의 통신을 통해 사용자 차량의 현재 상태, 차량의 이상 유무, 소모품 교환 시기, 차량사고 시 영상 및 급발진 추정 시 차량상태, 위치, 이동거리, 운전자의 발쪽 영상 저장 및 재생 등 사용자가 필요로 하는 다양한 기능과 정보들을 블랙박스 하나로 통합하여 구현 할 것이다. 이 시스템이 개발되면 차량 관리에 서툰 여성 운전자들, 좀 더 체계적이고 손쉽게 내 차 정보를 원하는 운전자들과 전 세계 자동차 소비자들의 관심사인 급발진에 대한 사고 분석이 정확해 질 것으로 여겨진다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.290-293
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• 2013

Due to vehicle personal injury accident does not, and to analyze the cause and prevention of a variety of devices and technologies are coming out. Among other things, representative of the black box, and rear camera. Despite these advances in technology, and vehicle human injuries continue to occur. The reason for this is that the children, the negligence of the driver or the vehicle is in reverse when a person suddenly passed the back of the vehicle, or the rear of the existing detection system is properly detected was unable to. Therefore, In this paper, we want to design a black box to reveal the cause of the accident, using Kinect to prevent accidents, rear-view camera and rear integrated black box system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.651-658
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• 2015

Recently, lots of research works have been actively focused on the self-driving car. In order to implement the self-driving car, lots of fusion techniques should be merged and, specially, it is noted that a vehicle-attached camera can provide several useful functionalities such as traffic lights recognition, pedestrian detection, stop-line recognition including simple driving records. Accordingly, as one of the efficient tools for the self-driving car implementation, this paper proposes a mathematical model for estimating effectively the road distance with a vehicle-attached black box camera. The proposed model can be effectively used for estimating the road distance by using the height of black box camera or the widths of the referenced road line and the observed road line. Through several simulations, it is shown that the proposed model is effective in estimating the road distance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.171-182
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• 2019

Bicycles are a pollution-free means of transportation. In addition to leisure, the use of bicycles is increasing as alternative eco-friendly transportation. Accordingly, bicycle accidents are also increasing. The purpose of this study is to implement bicycle black box technology to identify situation when a bicycle accident occurs. Currently, bicycle black box products are mainly based on video cameras, and are commercially available by adding various functions mainly on high resolution cameras and are sold at high prices. If a bicycle accident occurs, quantitative data on the accident location at the time of the accident and the state of the bicycle at the time of the accident is required. In this study, IMU sensor used to obtain acceleration and slope, and time and coordinates are obtained. In addition, real-time acceleration and tilt data while is stored in memory card and by using Bluetooth transmit to the smart phone owned by the in real time to prevent accidents and to monitor status.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.8-19
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• 2016

Potholes are caused by the presence of water in the underlying soil structure, which weakens the road pavement by expansion and contraction of water at freezing and thawing temperatures. Recently, automatic pothole detection systems have been studied, such as vibration-based methods and laser scanning methods. However, the vibration-based methods have low detection accuracy and limited detection area. Moreover, the costs for laser scanning-based methods are significantly high. Thus, in this paper, we propose a new pothole detection system using a commercial black-box camera. Normally, the computing power of a commercial black-box camera is limited. Thus, the pothole detection algorithm should be designed to work with the embedded computing environment of a black-box camera. The designed pothole detection algorithm has been tested by implementing in a black-box camera. The experimental results are analyzed with specific evaluation metrics, such as sensitivity and precision. Our studies confirm that the proposed pothole detection system can be utilized to gather pothole information in real-time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.133-146
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• 2023

Traffic states in urban areas are essential to implement effective traffic operation and traffic control. However, installing traffic sensors on numerous road sections is extremely expensive. Accordingly, estimating the traffic state using a vehicle-mounted camera, which shows a high penetration rate, is a more effective solution. However, the previously proposed methodology using object tracking or optical flow has a high computational cost and requires consecutive frames to obtain traffic states. Accordingly, we propose a method to detect vehicles and lanes by object detection networks and set the region between lanes as a region of interest to estimate the traffic density of the corresponding area. The proposed method only uses less computationally expensive object detection models and can estimate traffic states from sampled frames rather than consecutive frames. In addition, the traffic density estimation accuracy was over 90% on the black box videos collected from two buses having different characteristics.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1079-1084
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• 2018

The vehicle's black box is not suitable for bicycle black box because they contain sensors that are not suitable for bicycle black box. Therefore, this study implemented a system for viewing a video with a mobile phone as the slope changes. The angle was measured by using the gyro sensor and this angle was saved based on the angle setting on the raspberry pi. It has developed a system that allows users to download saved video files through mobile phone applications via FTP.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.40-48
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• 2018

This paper proposes a license-plate recognition algorithm for automobile black-box image which is obtained from the camera moving with the automobile. The algorithm intends to increase the overall recognition-rate of the license-plate by increasing the Korean character recognition-rate using multi-stage neural network for automobile black-box image where there are many movements of the camera and variations of light intensity. The proposed algorithm separately recognizes the vowel and consonant of Korean characters of automobile license-plate. First, the first-stage neural network recognizes the vowels, and the recognized vowels are classified as vertical-vowels('ㅏ','ㅓ') and horizontal-vowels('ㅗ','ㅜ'). Then the consonant is classified by the second-stage neural networks for each vowel group. The simulation for automobile license-plate recognition is performed for the image obtained by a real black-box system, and the simulation results show the proposed algorithm provides the higher recognition-rate than the existing algorithms using a neural network.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.671-677
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• 2012

A black-box program was implemented in order to monitor abnormal symptoms of human body irregularly occurring during sleep. The system consists of sensor probing body signals, auxiliary devices such as the alarm, lamp, network camera, and signal monitoring computer. Various types of sensors, PPG, ECG, EEG, temperature, respiration sensor, G-sensor, and microphone were used to more exactly identify the causes of abnormal symptoms. If a symptom occurs, the system records the patient's condition to provide information being utilized in the treatment. The sensors are attached on some locations of body being proper to check a specific type of abnormal reaction. Based on the normal range and type of measurement data, criteria of signal levels were set to distinguish abnormal reaction. An abnormal signal being probed, the program starts to operate the lamp, alarm, and network camera at the same time and stores the signal and video data.