• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.221.2-221.2
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• 2012

우주물체 전자광학 감시체계(OWL: Optical Wide-field Patrol)는 관측소들의 자동운영을 통한 인공위성의 궤도정보추출이 목적이다. 이를 위해 각각의 관측소에서 매일 밤 운영되어야 하는 관측명령을 자동으로 생성하는 스케줄러를 개발하였다. 스케줄러는 OWL 본부가 설치될 한국천문연구원의 NOS(Network Operating System) 서버에 설치 운영된다. 스케줄러는 사용자가 정한 관측대상 인공위성의 관측우선순위와 OC(Orbit Calculation) 서브시스템이 제공한 위성궤도정보를 바탕으로, 시간에 따른 관측수행내용을 기록한 관측명령서(OCF: Observation Command File)를 작성한다. 작성된 OCF는 각 관측소가 관측을 시작하기 전까지 해당 관측소로 전달되며, 관측소는 OCF를 바탕으로 관측을 수행하게 된다. 스케줄러는 "제한조건반영" 및 "OCF작성" 등 2부분으로 구성된다. "제한조건반영"은 관측시스템의 특징을 스케줄러에 반영하는 단계로써 시야각 등 광학계의 특징, 필터 등 주변 장비의 특징, CCD 카메라의 노출대기시간 등 검출기의 특징 등이 이에 포함된다. 사용자는 장비의 교체 및 개선 등 관측시스템 변경이 발생하는 경우 "제한조건반영"에 이를 적용함으로써 스케줄러가 새로운 시스템에 쉽게 적용할 수 있다. "OCF작성"은 "제한조건반영"의 내용을 바탕으로 관측대상위성을 선정하는 , 위성 관측 구간 중 최대한 많은 노출 횟수를 산출하는 , 한 장의 영상에서 최대한 많은 궤도 정보를 획득하기 위한 등 3개의 알고리즘에 의해 OCF를 작성한다.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.205.1-205.1
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• 2012

러시아 발사체 드네프르에 의해 발사될 과학기술위성 3호의 주탑재체 다목적적외선영상시스템, MIRIS (Multipurpose InfraRed Imaging System)는 한국천문연구원에서 주관하여 개발되었다. 그 구성 카메라인 EOC (Earth Observation Camera)는 한반도재난감시를 수행하고, SOC (Space Observation Camera)는 우리 은하 평면의 근적외선 서베이 관측을 통해 $360^{\circ}{\times}6^{\circ}$ Paschen-${\alpha}$ 방출선 지도를 작성하고 I, H 밴드 필터를 이용해서 황도 남북극에 대한 적외선우주배경복사를 관측한다. MIRIS 비행모델이 제작 완료되었고, 그 구성 기기인 SOC, EOC, 전장박스에 대한 최종 우주환경시험을 수행하였다. 과학기술위성 3호의 비행모델 우주환경시험은 진동시험과 열진공시험으로 이뤄지며, 그 시험 규격은 문서에 규정된 Acceptance Level로 수행된다. 충격시험은 공학인증모델을 통해 검증되었다. 열진공시험은 한국천문연구원에서 수행되었으며, 진동시험은 한국과학기술원 인공위성센터에서 수행되었다. 또한 전체 위성이 조립된 후 과학기술위성 3호의 열진공시험은 한국항공우주연구원에서 수행되었다. 이 발표에서는 MIRIS 비행모델에 대한 환경시험과정 및 결과를 보고하고, 과학기술위성이 전체적으로 조립된 후의 MIRIS 진동 및 열진공 시험 결과도 함께 논의한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.24-32
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• 2010

In this paper we proposed an object tracking system that detects the abandoned and removed objects, which is to be used in the intelligent surveillance applications. After the GMM based background subtraction and by using histogram method, the static region is identified to detect abandoned and removed objects. Since the system is implemented on DSP chip, it operates in realtime and is programmable. The input videos used in the experiment contain various indoor and outdoor scenes, and they are categorized into three different complexities; low, midium and high. By 10 times of experiment, we obtained high detection ratio at low and medium complexity sequences. On the high complexity video, successful detection ratio was relatively low because the scene contains crowdedness and repeated occlusion. In the future work, these complicated situation should be solved.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.293-300
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• 2009

The rotorcraft-based unmanned aerial vehicle(UAV) capable of performing close-range surveillance and reconnaissance has been developed. Trade studies on mission feasibility led to the adoption of a coaxial rotorcraft with twin rotors counter-rotating in one axis and driven by electric motors. A commercial off-the-shelf flight control computer(FCC) and a radio frequency modem were adopted for autonomous navigation. In order to achieve an aerial view, commercial charge-coupled device camera was also integrated into the vehicle. The performance of the completed vehicle was proved with manual flight test, and mission capability was verified through waypoint navigation flight after being equipped with FCC. This paper treats the whole process of design and system integration for development of the coaxial rotorcraft UAV.

• Smart Media Journal
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• v.5 no.1
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• pp.24-29
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• 2016

To satisfy the needs of high-level intelligent surveillance system, it shall be able to extract objects and classify to identify precise information on the object. The representative method to identify one's identity is face recognition that is caused a change in the recognition rate according to environmental factors such as illumination, background and angle of camera. In this paper, we analyze the robust face recognition of face image by changing the distance through a variety of experiments. The experiment was conducted by real face images of 1m to 5m. The method of face recognition based on Linear Discriminant Analysis show the best performance in average 75.4% when a large number of face images per one person is used for training. However, face recognition based on Convolution Neural Network show the best performance in average 69.8% when the number of face images per one person is less than five. In addition, rate of low resolution face recognition decrease rapidly when the size of the face image is smaller than $15{\times}15$.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.263-269
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• 2010

This paper describes real-time detection and tracking of moving objects for unmanned visual surveillance. Using images obtained from the fixed camera it detects moving objects within the image and tracks them with displaying rectangle boxes enclosing the objects. Tracking method is implemented on an embedded system which consists of TI DSK645.5 kit and the FPGA board connected on the DSP kit. The DSP kit processes image processing algorithms for detection and tracking of moving objects. The FPGA board designed for image acquisition and display reads the image line-by-line and sends the image data to DSP processor, and also sends the processed data to VGA monitor by DMA data transfer. Experimental results show that the tracking of moving objects is working satisfactorily. The tracking speed is 30 frames/sec with 320x240 image resolution.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.4
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• pp.49-60
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• 2008

The current VIPS are not effective in safety point of view, because they are originally developed for mimicking loop detectors. Therefore, it is important to identify vehicle trajectories in real time, because recognizing vehicle movements over a detection zone enables to identify which situations are hazardous, and what causes them to be hazardous. In order to improve limited safety functions of the current VIPS, this research has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, incident detection and conflict as well as traffic information via tracking image detectors. This system is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of various traffic situations. Experiments were conducted for measuring the cases of incident detection and abnormal vehicle trajectory with rapid lane change.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.115-121
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• 2018

Fluorescent images using near-infrared light have no worry about radioactivity, and images can be checked in real time during surgery. Therefore experiments using fluorescent images for monitoring lymph node biopsy are actively under way. Fluorescent imaging equipment uses high heat-generating components such as LED and camera, thus uses water-cooling system as a stable heating suppression means. However in the fluorescent image equipment, the water cooling system takes a large volume which is a disadvantage in terms of miniaturization of the equipment. Even if the air cooling system is used for miniaturizing the equipment, heat generation is a problem. In this paper, we have experimented with the air cooling method using PWM control for the miniaturization of the equipment, and confirmed the constant quality of the fluorescent image and the suppression of the heat generation without any problems even when the equipment is used for a long time.

• Journal of IKEEE
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• v.23 no.3
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• pp.777-784
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• 2019

A pre-fire awareness and automatic notification system are required because it is possible to minimize the damage if the fire situation is precisely detected after a fire occurs in a place where people are unusual or in a mountainous area. In this study, we developed a RaspberryPi-based fire recognition system using Faster-recurrent convolutional neural network (F-RCNN) and single shot multibox detector (SSD) and demonstrated a fire alarm system that works with power line communication. Image recognition was performed with a pie camera of RaspberryPi, and the detected fire image was transmitted to a monitoring PC through an inductive power line communication network. The frame rate per second (fps) for each learning model was 0.05 fps for Faster-RCNN and 1.4 fps for SSD. SSD was 28 times faster than F-RCNN.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.672-678
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• 2020

Ultra-high-speed images that accurately depict the minute movements of objects have become common as low-cost and high-performance cameras that can film at high speeds have emerged. In this paper, the proposed method removes unexpected noise contained in images after input at high speed, and then extracts an area of interest that can represent personal information, such as skin areas, from the image in which noise has been removed. In this paper, noise generated by abnormal electrical signals is removed by applying bilateral filters. A color model created through pre-learning is then used to extract the area of interest that represents the personal information contained within the image. Experimental results show that the introduced algorithms remove noise from high-speed images and then extract the area of interest robustly. The approach presented in this paper is expected to be useful in various applications related to computer vision, such as image preprocessing, noise elimination, tracking and monitoring of target areas, etc.