• 한국콘텐츠학회논문지
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• 제7권2호
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• pp.40-50
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• 2007

본 논문에서는 카메라로부터 획득 되어진 비디오 시퀀스로부터 다중 움직임 객체와 배경을 분할하고 시공간 정보에 기반 한 객체 추적 방법을 제안한다. 제안한 방법은 3단계로 구성되어 있다. 먼저 입력 비디오 시퀀스로부터 프레임 사이의 차를 이용한 움직임 영역과 움직임이 존재하지 않는 영역을 구분하여 적응적 경계간을 추출한다. 두 번째는 참조 배경영상과 적응적 경계값을 이용하여 움직임이 존재하는 영역으로부터 개략적 객체 분할을 수행하며, 분할된 이진영상에 형태학적 영역 병합 알고리즘을 적용하여 객체 병합을 수행하였다. 마지막으로 분할된 객체에 시공간 정보를 이용하여 객체에 임의의 ID를 할당하여 추적하였다. 카메라로부터 획득되어진 비디오 시퀀스를 이용한 실험에서 객체들의 분할 및 추적의 효율성과 시스템의 유용성을 확인하였다.

• 융합정보논문지
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• 제10권10호
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• pp.123-127
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• 2020

야간 중대형 산불의 전체적인 화선정보를 얻기 위하여 누락되는 산불에 대한 반복적인 임무비행과 임무수행 종료 후 획득되는 정사영상 정합에 대한 소요시간을 줄이기 위하여 실시간 동영상으로 산불발생 여부를 판단하고 드론의 위치와 영상카메라의 각도정보, 지도상의 고도정보를 활용하여 판단된 산불위치를 계산하여 지도에 도시할 수 있는 지상통제시스템을 개발하였다. 개발된 기능의 신뢰성을 검증하기 위하여 비행고도 별, 영상카메라의 지향하는 위치정보의 오차거리를 측정하였으며, 신뢰할 수 있는 범위내의 위치정보를 지도에 표시하였다. 본 논문에 개발된 기능으로 다수의 산불 발생위치를 실시간 식별이 가능하므로 산불 진화대책 수립을 위한 전체적인 화선정보를 보다 신속하게 획득할 수 있을 것으로 예상된다.

• 공학교육연구
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• 제24권4호
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• pp.21-29
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• 2021

The objective of this research is to devise a BARS evaluation system as a performance evaluation plan for non-face-to-face capstone design and to verify the validity through the expert FGI as the remote education is highlighted as a new normal standard in the post corona epoch. The conclusion of this research is as follows. First, the non-face-to-face capstone design is a competency centered subject that allows you to develop the engineering and majoring knowledge and its function and attitude, and the achievement of program outcome is the objective competency, and the researcher proposes the BARS method evaluation, one of competency evaluation method, as a new performance evaluation plan. Second, for the evaluation of PO achievement of non-face-to-face capstone design, the researcher deduced 20 behavior identification standard(anchor) of BARS evaluation system, and developed the achievement standard per 4 levels. Third, as the evaluation tool of non-face-to-face capstone design, the presentation data(PPT), presentation video, product such as trial product(model), non-face-to-face class participation video, discussion participating video, team activity report, and result report for the evidential data of BARS evaluation were appeared as proper. Finally, the BARS evaluation plan of non-face-to-face capstone design would be efficiently made through the establishment of evaluation plan, the establishment of grading standard of BARS evaluation scale, the determination of evaluation subject and online BARS evaluation site.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• 한국통신학회논문지
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• 제29권11C호
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• pp.1527-1540
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• 2004

본 논문은 트래픽 정보취득을 위하여 영상의 라인 샘플링을 이용한 고속이동물체 속도 측정 알고리즘을 제안하였다. 이동물체의 트래픽 정보 취득을 위한 속도 측정은 도로에 제 1 샘플라인과 제 2 샘플라인을 설정해 놓고, 물체가 샘플라인을 통과할 때 취득된 영상의 시변환 색조 데이터와 기준영상 색조 데이터 사이에서 차영상 기법으로 자동차를 검출하고, 자동차가 두 샘플라인 사이에 거리를 통과할 때 점유하는 프레임수로 속도를 측정하였다. 제 1 샘플라인과 제 2 샘플라인에서 각각 검출된 자동차의 색조로 동일성 판별을 하였다. 제안된 방법의 타당성을 검토하기 위하여 주행하는 자동차를 대상으로 동일성 판별 및 속도 측정을 한 결과, 동일성 판별은 두 개의 샘플링 라인을 통과하는 자동차의 색조 데이터로 판별됨을 보였고, 자동차의 속도 측정은 X-밴드 속도 측정 시스템과 비교한 결과 3% 이내임을 보였다.

• Journal of Animal Science and Technology
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• 제65권3호
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• pp.519-534
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• 2023

Rumination in cattle is closely related to their health, which makes the automatic monitoring of rumination an important part of smart pasture operations. However, manual monitoring of cattle rumination is laborious and wearable sensors are often harmful to animals. Thus, we propose a computer vision-based method to automatically identify multi-object cattle rumination, and to calculate the rumination time and number of chews for each cow. The heads of the cattle in the video were initially tracked with a multi-object tracking algorithm, which combined the You Only Look Once (YOLO) algorithm with the kernelized correlation filter (KCF). Images of the head of each cow were saved at a fixed size, and numbered. Then, a rumination recognition algorithm was constructed with parameters obtained using the frame difference method, and rumination time and number of chews were calculated. The rumination recognition algorithm was used to analyze the head image of each cow to automatically detect multi-object cattle rumination. To verify the feasibility of this method, the algorithm was tested on multi-object cattle rumination videos, and the results were compared with the results produced by human observation. The experimental results showed that the average error in rumination time was 5.902% and the average error in the number of chews was 8.126%. The rumination identification and calculation of rumination information only need to be performed by computers automatically with no manual intervention. It could provide a new contactless rumination identification method for multi-cattle, which provided technical support for smart pasture.

• 방송공학회논문지
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• 제27권6호
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• pp.936-939
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• 2022

최근 공중 전투체계 기술들이 발전함에 따라 대공방어 시스템의 발전이 요구되고 있다. 대공 방어 시스템의 운용개념에 있어, 표적에 적합한 무장을 선택하는 것은 제한된 대공 전력을 사용하여 위협체에 대해 효율적으로 대응한다는 측면에서 체계에 요구되는 능력 중 하나이다. 비행 위협체의 식별에 있어 많은 부분이 운용자의 육안 식별에 의존하는데 고속으로 기동하고 원거리에 위치한 비행체를 육안으로 판별하는 것은 많은 한계가 있다. 뿐만 아니라, 현대 전장에서 무인화 및 지능화된 무기체계의 수요가 증가함에 따라 운용자의 육안 식별 대신 체계가 자동으로 비행체를 식별하고 분류하는 기술의 개발이 필수적이다. 영상자료를 수집해 딥러닝 기반의 모델을 이용하여 무기체계를 식별한 사례로는 전차와 함정 등이 있지만 비행체의 식별에 대한 연구는 아직 많이 부족한 상황이다. 따라서 본 논문에서는 합성곱 신경망 모델을 이용하여 전투기, 헬기, 드론을 분류하는 모델을 제시하고 제시하는 모델의 성능을 분석한다. 본 논문에서 제시하는 모델은 시험세트에 대해 95% 이상의 정확도를 보이고, precision 0.9579, recall 0.9558, F1-socre 0.9568의 값을 나타내는 것을 확인할 수 있다.

• 한국항해학회지
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• 제24권5호
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• pp.435-441
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• 2000

This paper deals with the development of RACOM(Radar Signal Detecting ＆ Processing Computer). RACOM is a radar display system specially designed for radar scan conversion, signal processing and PCI radar image display. RACOM contains two components； i )RSP(Radar Signal Processor) board which is a PCI based board for receiving video, trigger, heading & bearing signals from radar scanner ＆ tranceiver units and processing these signals to generate high resolution radar image, and ⅱ）Applications which perform ordinary radar display functions such as EBL, VRM and so on. Since RACOM is designed to meet a wide variety of specifications(type of output signal from tranceiver unit), to record radar images and to distribute those images in real time to everywhere in a networked environment, it can be applicable to AIS(Automatic Identification System) and VDR(Voyage Data Recorder).

• Journal of Information Processing Systems
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• 제5권2호
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• pp.41-68
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• 2009

Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

• 센서학회지
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• 제24권1호
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• pp.54-61
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• 2015

In recent years, there has been increasing use of automatic surveillance and monitoring systems based on vision sensors. Humans are often the most important target in the systems, but processing human images is difficult due to the small sizes and flexible motions. Particularly, occlusion among pedestrians in camera images brings practical problems. In this paper, we propose a novel method to separate image regions of occluded pedestrians. A camera equipped with a wide angle lens is attached to the ceiling of a building corridor for sensing pedestrians with a wide field of view. The output images of the camera are processed for the human detection, tracking, identification, distortion correction, and occlusion handling. We resolve the occlusion problem adaptively depending on the angles and positions of their heads. Experimental results showed that the proposed method is more efficient and accurate compared with existing methods.