• Journal of Biomedical Engineering Research
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• v.20 no.1
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• pp.75-80
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• 1999

Since a Holter monitor records a patients' ECG when he is in motion, it can monitor complex heart disease effectively. But it can not handle a sudden heart disease because the diagnosis process will be done only after 24 hours. So in this study, a model of ambulatory monitoring system using wireless data communication network is proposed. And a mobile ECG equipment and a doctors' terminal are developed for the proposed system implementation. As a result, we can evaluate that the proposed ambulatory monitoring system is suitable for the management of ambulatory patients who may be at risk form sudden cardiac abnormalities.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1551-1553
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• 2013

지능형 영상감시에 대한 연구와 개발이 가속화되면서, 화재감시에 대한 예방 및 경보시스템 또한 빠른 속도로 발전하고 있다. 그러나, 기존시스템은 화재에 대한 감시기능에만 국한되어 있어, 기능으로써의 한계점을 가지고 있다. 따라서 본 논문에서는 이러한 문제점을 해결하기위해, 본 논문에서는 화재와 보행자를 검출하여, 초기대응 및 화재원인에 대한 정보를 제공하며, 보행자에 대한 안전을 최대한 보장할 수 있는 화재현장 보행자 인식을 통한 지능형 영상인식 시스템을 설계하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.435-442
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• 2015

In this paper, we propose a tracking and recognition of pedestrian/vehicle for intelligent multi-visual surveillance system. The intelligent multi-visual surveillance system consists of several fixed cameras and one calibrated PTZ camera, which automatically tracks and recognizes the detected moving objects. The fixed wide-angle cameras are used to monitor large open areas, but the moving objects on the images are too small to view in detail. But, the PTZ camera is capable of increasing the monitoring area and enhancing the image quality by tracking and zooming in on a target. The proposed system is able to determine whether the detected moving objects are pedestrian/vehicle or not using the SVM. In order to reduce the tracking error, an improved camera calibration algorithm between the fixed cameras and the PTZ camera is proposed. Various experimental results show the effectiveness of the proposed system.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1365-1368
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• 2015

컴퓨터 비전 분야의 활용영역과 시장성이 증대하면서 가장 많이 사용되는 객체인식 및 탐지 기술과 관련된 연구는 꾸준히 진행되고 있다. 최근에는 ADAS(Advanced Driver Assistance Systems)와 특징적인 객체를 인식 추적할 수 있는 지능형 감시시스템에서의 가장 핵심적인 기술로 자리 잡고 있다. 본 연구에서는 보행자 탐지에 사용하는 특징들 중에서 조명변화에 강건한 HOG와 Cascade-Adaboost를 기반으로 보행자 탐지 모델을 후보영역을 검출하고 검출된 영역에서 컬러정보를 추출하여 의사결정 트리에 적용시켜 최종 보행자를 탐지하는 시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1544-1546
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• 2013

도시철도는 대표적인 대중교통으로써, 하루에도 수백만명의 승객들이 이용하고 있다. 따라서 도시철도를 이용하는 승객들의 안전이 보장되어야 하며, 안전한 서비스 제공 및 예방 노력이 제반되어야 한다. 이를 위해 설치된 폐쇄회로 CCTV와 상황실은 넓은 구역내의 모든 위험상황을 감지하고 대응하기에 미흡하다. 따라서 이러한 영상감시의 미흡한 점을 보완하여 기설치된 CCTV를 통해 위험구역내 보행자를 자동으로 인지하여 큰 사고를 미연에 방지하고자 역사내 지능형 영상감시 시스템을 설계하였다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1351-1352
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• 2015

본 논문에서는 지능형 영상 감시 시스템에서 보행자를 검출하고 추적을 수행하기 위해 은닉층 활성함수에 가우시안 대신 FCM를 사용한 RBFNNs 패턴분류기와 객체 추적 알고리즘인 Mean Shift를 융합한 시뮬레이터를 개발한다. 시뮬레이터는 검출부과 추적부로 나누며, 검출부에서는 입력 영상으로부터 기울기의 방향성을 이용한 HOG(Histogram of Oriented Gradient) 특징을 구하고 빠른 처리속도를 위해 PCA 알고리즘을 통해 차원수를 축소하고 pRBFNNs 패턴분류기를 통해 보행자를 검출 한다. 다음 추적부에서 객체 추적 알고리즘인 Mean Shift를 이용하여 검출된 보행자 추적을 수행한다.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.26 no.4
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• pp.73-78
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• 2008

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.259-266
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• 2016

Recently, CCTV cameras are emplaced actively to reinforce security and intelligent surveillance systems have been under development for detecting and monitoring of the objects in the video. In this study, we propose a method for detection of upper body in intelligent surveillance system using FCM-based RBFNN classifier realized with the aid of HOG features. Firstly, HOG features that have been originally proposed to detect the pedestrian are adopted to train the unique gradient features about upper body. However, HOG features typically exhibit a very high dimension of which is proportional to the size of the input image, it is necessary to reduce the dimension of inputs of the RBFNN classifier. Thus the well-known PCA algorithm is applied prior to the RBFNN classification step. In the computer simulation experiments, the RBFNN classifier was trained using pre-classified upper body images and non-person images and then the performance of the proposed classifier for upper body detection is evaluated by using test images and video sequences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.812-820
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• 2012

In this paper, multiple pedestrians tracking system using Histogram of Oriented Gradient and occlusion detection is proposed. The proposed system is applicable to Intelligent Surveillance System. First, we detect pedestrian in a image sequence using pedestrian's feature. To get pedestrian's feature, we make block-histogram using gradient's direction histogram based on HOG(Histogram of Oriented Gradient), after that a pedestrian region is classified by using Linear-SVM(Support Vector Machine) training. Next, moving objects are tracked by using position information of the classified pedestrians. And we create motion trajectory descriptor which is used for content based event retrieval. The experimental results show that the proposed method is more fast, accurate and effective than conventional methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.58-70
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• 2019

In the case of a pedestrian traffic accident, it has a large-scale danger directly connected by a fatal accident at the time of the accident. The domestic ITS is not used for intelligent risk classification because it is used only for collecting traffic information despite of the construction of good quality traffic infrastructure. The CNN based pedestrian detection classification model, which is a major component of the proposed system, is implemented on an embedded system assuming that it is installed and operated in a restricted environment. A new model was created by improving YOLO's artificial neural network, and the real-time detection speed result of average accuracy 86.29% and 21.1 fps was shown with 20,000 iterative learning. And we constructed a protocol interworking scenario and implementation of a system that can connect with the ITS. If a pedestrian accident prevention system connected with ITS will be implemented through this study, it will help to reduce the cost of constructing a new infrastructure and reduce the incidence of traffic accidents for pedestrians, and we can also reduce the cost for system monitoring.