• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2008년도 춘계 종합학술대회 논문집
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• pp.116-120
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• 2008

스마트 카메라는 종래의 획득한 영상을 압축하여 전송하는 네트워크 카메라 기능에 더하여, 획득한 영상을 해석하여 상황을 인지하고 이에 따른 실시간 조치가 가능한 지능 비젼 기능을 추가적으로 갖춘 카메라이다. 지능 비젼 알고리즘들은 연산량이 많다. 따라서 싱글 CPU로 영상을 압축하고 전송하는 일 뿐만 아니라 지능 비젼 처리까지 모두 실시간으로 처리하기에는 무리가 있다. Texas Instruments 사가 제공하는 다빈치 프로세서는 ARM 코어와 DSP 코어의 듀얼 코어이며 네트워킹 인터페이스 및 비디오 획득 인터페이스를 비롯하여 디지털 비디오 응용 임베디드 제품 개발에 필요한 다양한 I/O을 지원하는 인기 있는 ASSP(Application Specific Standard Product)이다. 본 논문에서는 다빈치 프로세서 기반 스마트 카메라의 S/W 를 설계하고 구현한 결과를 기술한다. 얼굴 검출 응용을 예로 구현하였고 동작이 잘 수행됨을 확인하였다. 향후 보다 광범위하고 실시간으로 동작되는 비젼 기능이 지원되는 스마트 카메라 개발을 위해 보다 효율적인 비젼 응용 S/W 구조와 알고리즘의 최적화에 대한 연구가 필요하다.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제13권7호
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• pp.488-498
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• 2007

본 논문은 유비쿼터스 스마트 공간에서 중첩 FOV와 비중첩 FOV에 대한 카메라 네트워크의 시공간 위상을 표현하는 새로운 방법을 제안한다. 제안된 방법을 이용하여 다중 카메라들간의 움직이는 객체들을 인식 및 추적하였으며 이를 통해 카메라 네트워크의 위상을 결정하였다. 다중 카메라의 영상으로부터 여러 객체들을 추적하기 위해 여러 가지 방법들을 사용하였다. 우선, 단일 카메라에서 객체들의 겹침 문제를 해결하기 위해서 병합-분리(Merge-Split) 방법을 사용하였으며, 보다 정확한 객체 특성을 추출하기 위해 그리드 기반의 부분 추출 방법을 사용하였다. 또한, 비중첩 FOV를 포함하는 다중 카메라의 보이지 않는 지역에 대한 객체 추적을 위해 등장과 퇴장 영역간의 전이시간과 사람들의 외형 정보를 고려하였다. 본 논문에서는 다양한 등장과 퇴장 영역간의 전이시간을 추정하고 전이확률을 이용하여 무방향 가중치 그래프로써 카메라 위상을 가시적으로 표현하였다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 춘계학술대회 논문집
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• pp.1722-1728
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• 2011

The security issue in urban transit system has been widely considered as the common matters after the fire accident at Daegu subway station. The safe urban transit system is highly demanded because of the vast number of daily passengers, and it is one of the most challenging projects. We introduced a test model for integrated security system for urban transit system and built it at a subway station to demonstrate its performance. This system consists of cameras, sensor network and central monitoring software. We described the smart camera functionality in more detail. The proposed smart camera includes the moving objects recognition module, video analytics, video encoder and server module that transmits video and audio information.

• 한국위성정보통신학회논문지
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• 제10권2호
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• pp.70-74
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• 2015

본 논문은 위성통신 기반의 위성 영상감시 센서 네트워크 적용을 위한 스마트 비젼 센서에 대해 기술한다. 스마트 비젼센서 단말은 현장에서 산불, 연기, 침입자 움직임 등의 이벤트를 자동감지하면서 높은 성능 신뢰도, 견고한 하드웨어 내구성, 용이한 유지보수, 끊김없는 통신유지 기능들이 요구된다. 이러한 요구사항들을 만족시키기 위하여 스마트 비젼 센서가 내장된 초소형 위성통신 단말을 제안하며 위성 송수신 기능과 더불어 고 신뢰도의 임베디드 영상분석 및 영상압축 기능을 처리한다. 제안하는 비젼 센서 알고리즘의 컴퓨터 시뮬레이션과 비젼 센서 시제품 시험을 통하여 영상감시 성능을 검증하였으며 실용성을 확인하였다.

• 한국멀티미디어학회논문지
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• 제20권5호
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• pp.769-781
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• 2017

Deep learning networks like Convolutional Neural Networks (CNNs) show successful performances in many computer vision applications such as image classification, object detection, and so on. For implementation of deep learning networks in embedded system with limited processing power and memory, deep learning network may need to be simplified. However, simplified deep learning network cannot learn every possible scene. One realistic strategy for embedded deep learning network is to construct a simplified deep learning network model optimized for the scene images of the installation place. Then, automatic training will be necessitated for commercialization. In this paper, as an intermediate step toward automatic training under fisheye camera environments, we study more precise human localization in fisheye images, and propose an accurate human localization method, Automatic Ground-Truth Labelling Method (AGTLM). AGTLM first localizes candidate human object bounding boxes by utilizing GoogLeNet-LSTM approach, and after reassurance process by GoogLeNet-based CNN network, finally refines them more correctly and precisely(tightly) by applying saliency object detection technique. The performance improvement of the proposed human localization method, AGTLM with respect to accuracy and tightness is shown through several experiments.

• 한국통신학회논문지
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• 제40권1호
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• pp.152-164
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• 2015

최근 반도체 기술, 센서 기술 및 이동통신 기술의 발전으로 스마트 자동차 기술 연구 개발이 진행 중에 있다. 사회가 발전함에 따라 차량이 증가하였고 사고에 대한 위험은 점차 높아지고 있다. 그에 따라 기존의 차량용 블랙박스 외에 차량의 각종 센서 정보를 활용하여 운전자에게 다양한 정보를 제공하는 첨단 운전자 보조 시스템이 연구되고 있다. 본 논문에서는 차량 간의 통신기능을 포함하고, 주변의 정보를 습득하여 제공할 수 있는 스마트 카메라 장치를 설계 및 구현하여, 장치에 포함된 카메라로부터 입력 받은 영상을 분석하여 획득한 정보를 영상 메타데이터화 하는 기술에 대한 연구를 수행하였다. 또한 임베디드 장치의 제한된 계산 성능을 보완하기 위해 관심영역을 설정하는 S-ROI(Static-Region Of Interest), D-ROI(Dynamic-Region Of Interest) 방식을 고안하였다. 실험을 통해 영상처리 속도가 전체영상 분석에 비해 S-ROI의 경우 3.0배, D-ROI의 경우 4.8배 향상함을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.1967-1983
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• 2020

The goal of this paper is to analyze and build an algorithm to recognize hand gestures applying to smart home applications. The proposed algorithm uses image processing techniques combing with artificial neural network (ANN) approaches to help users interact with computers by common gestures. We use five types of gestures, namely those for Stop, Forward, Backward, Turn Left, and Turn Right. Users will control devices through a camera connected to computers. The algorithm will analyze gestures and take actions to perform appropriate action according to users requests via their gestures. The results show that the average accuracy of proposal algorithm is 92.6 percent for images and more than 91 percent for video, which both satisfy performance requirements for real-world application, specifically for smart home services. The processing time is approximately 0.098 second with 10 frames/sec datasets. However, accuracy rate still depends on the number of training images (video) and their resolution.

• International Journal of Internet, Broadcasting and Communication
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• 제12권2호
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• pp.8-14
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• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.

• Smart Structures and Systems
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• 제24권6호
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• International Journal of Internet, Broadcasting and Communication
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• 제12권2호
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• pp.1-7
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• 2020

The secure access the lighting, Heating, ventilation, and air conditioning (HVAC), fire safety, and security control boxes of building facilities is the primary objective of future smart buildings. This paper proposes an authorized user access to the electrical, lighting, fire safety, and security control boxes in the smart building, by using color grid coded optical camera communication (OCC) with face recognition Technologies. The existing CCTV subsystem can be used as the face recognition security subsystem for the proposed approach. At the same time a smart device attached camera can used as an OCC receiver of color grid code for user access authentication data sent by the control boxes to proceed authorization. This proposed approach allows increasing an authorization control reliability and highly secured authentication on accessing building facility infrastructure. The result of color grid code sequence received by the unauthorized person and his face identification allows getting good results in security and gaining effectiveness of accessing building facility infrastructure. The proposed concept uses the encoded user access authentication information through control box monitor and the smart device application which detect and decode the color grid coded informations combinations and then send user through the smart building network to building management system for authentication verification in combination with the facial features that gives a high protection level. The proposed concept is implemented on testbed model and experiment results verified for the secured user authentication in real-time.