• Journal of KIISE:Computer Systems and Theory
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• v.30 no.11
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• pp.610-620
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• 2003

Various shadows are one of main factors that cause errors in vision based vehicle detection. In this paper, two simple methods, land mark based method and BS & Edge method, are proposed for vehicle detection and shadow rejection. In the experiments, the accuracy of vehicle detection is higher than 96%, during which the shadows arisen from roadside buildings grew considerably. Based on these two methods, vehicle counting, tracking, classification, and speed estimation are achieved so that real-time traffic parameters concerning traffic flow can be extracted to describe the load of each lane.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.595-597
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• 2001

본 논문에서는 가속도 센서에 의해 사고를 검지하고 사고 전후의 영상정보를 저장하는 차량용 사고분석장치를 설계하였으며 이를 위한 사고 분석 시뮬레이터를 윈도우 기반에서 OpenGL 3차원 그래픽 라이브러리를 사용하여 구현하였다. 이는 알고리즘부와 디스플레이부로 구성되며, 알고리즘부에서는 도로 영상에 대한 영상처리를 수행한다. 여기서 개선된 역우너 근법에 의해 전처리된 영상을 필터링하여 차선을 검지하고, 검지된 차선을 이용하여 차선 파라미터들을 추출하며, 디스플레이부에서 추출된 파라미터들을 입력받아서 OpenGL 라이브러리 함수를 사용하여 사고를 3차원으로 재현한다.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.1
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• pp.7-15
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• 2016

Ocean wave parameters are important for safety and efficiency of operation and routing of marine traffic. In this paper, by using X-band marine radar, we try to develop an effective algorithm for collecting ocean surface information such as current velocity, wave parameters. Specifically, by exploiting iterative refinement flow instead of using fixed control schemes, an effective algorithm is designed in such a way that it can not only compute efficiently the optimized current velocity but also introduce new cost function in an optimized way. Experimental results show that the proposed algorithm is very effective in retrieving the wave information compared to the conventional algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.1
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• pp.32-39
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• 2016

Intelligent Transport Systems (Intelligent Transportation System: ITS) is a system for inducing a flow of ideal car for using the most advanced technology, it is determined the status of the road, and take appropriate action. In order to be measured at various time points, and is managed, the information about the traffic situation is used image using a computer mainly. The image processing using a computer, it is an easy way to collect parameters of the various traffic in real time, technology has developed more and more. Vehicle detection of transport parameters of intelligent transportation system is a very important technology basically. Therefore, technology detection method using car background images and the contour line extraction method using an edge is used, however, problems have been raised on the accuracy of the detection rate.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.5
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• pp.42-51
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• 2001

Vehicle detection is the basic of traffic monitoring. Video based systems have several apparent advantages compared with other kinds of systems. However, In video based systems, shadows make troubles for vehicle detection, especially active shadows resulted from moving vehicles. In this paper, a new method that combines background subtraction and edge detection is proposed for vehicle detection and shadow rejection. The method is effective and the correct rate of vehicle detection is higher than 98% in experiments, during which the passive shadows resulted from roadside buildings grew considerably. Based on the proposed vehicle detection method, vehicle tracking, counting, classification and speed estimation are achieved so that traffic parameters concerning traffic flow is obtained to describe the load of each lane.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.133-135
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• 2021

본 논문에서는 목조 문화재 영상에서의 변위 현상 중 하나인 크랙이 발생하는 영역을 감지하기 위한 임베딩 유사도 기반 모델을 제안한다. 우선 변위가 존재하지 않는 정상으로만 구성된 학습 이미지는 사전 학습된 합성 곱 신경망을 통과하여 임베딩 벡터들을 추출한다. 그 이후 임베딩 벡터들을 가지고 정상 클래스에 대한 분포의 파라미터 값을 구한다. 실제 추론 과정에 사용되는 테스트 이미지에 대해서도 마찬가지로 임베딩 벡터를 구한다. 그런 다음 테스트 이미지의 임베딩 벡터와 이전에 구한 정상 클래스를 대표하는 가우시안 분포 정보와의 거리를 계산하여 이상치 맵을 생성하여 최종적으로 변위가 존재하는 영역을 감지한다. 데이터 셋으로는 충주시 근처의 문화재에 방문해서 수집한 목조 문화재 이미지를 가지고 정상 및 비정상으로 구분한 데이터 셋을 사용하였다. 실험 결과 우리가 제안한 임베딩 유사도 기반 모델이 목조 문화재에서 크랙이 발생하는 변위 영역을 잘 감지함을 확인하였다. 이러한 결과로부터 우리가 제안한 방법이 목재 문화재의 크랙 현상에 대한 변위 영역 검출에 있어서 매우 적합함을 보여준다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.439-442
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• 2010

본 논문에서는 비전 기반의 실시간 대기오염 모니터링 시스템을 제안한다. 제안된 시스템은 먼저 실시간으로 제공되는 동영상을 분석하여 차종 별 대수와 평균속도 등의 교통 파라미터를 실시간으로 추출하고, 이를 바탕으로 대기 중의 CO, NO2등의 밀도를 추정하여 시간대별 대기 오염도를 모니터링 한다. 이를 위해 제안된 시스템은 배경모델을 이용한 차량 추출, 차종 별 윤곽선 및 크기 정보를 이용하여 템플릿 기반으로 차종을 인식하고 이를 추적하여 대수 및 속도를 인식한다. 제안된 시스템의 평가를 위해 교통이 밀집된 공간에 설치하여 테스트하였고, 실제 결과와 비교한 결과, 차량 속도에서 정확도 83.3%, 차종인식에서 정확도 86.98%를 보였다. 이러한 실험 결과는 제안된 시스템이 다양한 지역에서 실시간 대기오염물질 배출량을 산정하는데 적용될 수 있음을 보여주었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.4
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• pp.13-24
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• 2008

Real-time traffic data are readily obtainable by traffic surveillance systems of intelligent transportation systems (ITS). Such data greatly support further applications in the field of traffic operations, planning, and safety. However, traffic data should be appropriately processed to fully exploit the benefits of data collection capability. Rather than developing individual data processing techniques, which is major concern of existing studies, this study proposes a novel methodology for evaluating data processing techniques in an integrated manner. Also, a tool for implementing the proposed methodology is developed. Users can extract useful and more reliable traffic data based upon their ultimate purpose of data usage by the evaluation tool developed in this study. Actual freeway traffic data are, as an example, fed into the evaluation tool, and results are discussed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.07a
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• pp.310-311
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• 2015

Ships' tracking data are being monitored and collected by vessel traffic service center in real time. In this paper, we intend to contribute to vessel traffic service operators' decision making through extracting ships' tracking patterns and models based on these data. Support Vector Machine algorithm was used for vessel track modeling to handle and process the data sets and k-fold cross validation was used to select the proper parameters. Proposed data processing methods could support vessel traffic service operators' decision making on case of anomaly detection, calculation ships' dead reckoning positions and etc.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.285-286
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• 2016

We aim to propose the prediction modeling method of ship's position with extracting ship's trajectory model through pattern recognition based on the data that are being collected in VTS centers at real time. Support Vector Machine algorithm was used for data modeling. The optimal parameters are calculated with k-fold cross validation and grid search. We expect that the proposed modeling method could support VTS operators' decision making in case of complex encountering traffic situations.