• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.711-714
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• 2021

고정 카메라 환경에서 전경과 배경 간 픽셀값의 차를 이용하여 전경을 추출하기 위해서는 정확한 배경 영상이 필요하다. 또한, 프레임마다 변화하는 실제 배경과 맞추기 위해 배경 영상을 지속해서 갱신할 필요가 있다. 본 논문에서는 정확한 배경 영상을 생성하기 위해 실시간 처리가 가능한 딥러닝 기반 객체 탐지기의 결과를 입력받아 영상 처리에 활용함으로써 배경을 생성 및 지속적으로 갱신하고, 획득한 배경 정보를 이용해 전경을 추출하는 방법을 제안한다. 먼저, 고정 카메라에서 획득되는 비디오 데이터에 딥러닝 기반 객체 탐지기를 적용한 박스 단위 객체 탐지 결과를 지속적으로 입력받아 픽셀 단위의 배경 영상을 갱신하고 개선된 배경 영상을 도출한다. 이후, 획득한 배경 영상을 이용하여 더 정확한 전경 영상을 획득한다. 또한, 본 논문에서는 시설물에 가려진 객체를 더 정확히 탐지하기 위해서 전경 영상을 이용하여 시설물 영상을 추출하는 방법을 제안한다. 실제 돈사에 설치된 카메라로 부터 획득된 12시간 분량의 비디오를 이용하여 실험한 결과, 제안 방법을 이용한 전경과 시설물 추출이 효과적임을 확인하였다.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1075-1087
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• 2017

In this study, we tried to utilize results of the change detection analysis for satellite images as the basis for updating the land cover map. The Sequential Spectral Change Vector Analysis ($S^2CVA$) was applied to multi-temporal multispectral satellite imagery in order to extract changed areas, efficiently. Especially, we minimized the false alarm rate of unsupervised change detection due to the seasonal variation using the direction information in $S^2CVA$. The binary image, which is the result of unsupervised change detection, was integrated with the existing land cover map using the zonal statistics. And then, object-based analysis was performed to determine the changed area. In the experiment using PlanetScope data and the land cover map of the Ministry of Environment, the change areas within the existing land cover map could be detected efficiently.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1763-1770
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• 2023

The availability of high-resolution satellite image time series data has led to an increase in change detection research. Various methods are being studied, such as satellite image pixel and object-level change detection algorithms, as well as algorithms that apply deep learning technology. In this paper, we propose a QGIS plugin-based system to enhance the utilization of these useful results and present an actual implementation case. The proposed system is a system for intensive change detection and monitoring of areas of interest, and we propose a convenient system expansion method for algorithms to be developed in the future. Furthermore, it is expected to contribute to the construction of satellite image utilization systems by presenting the basic structure of commercialization of change detection research.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1427-1443
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• 2018

Object-based image analysis (OBIA) allows higher computation efficiency and usability of information inherent in the image, as it reduces the complexity of the image while maintaining the image properties. Superpixel methods oversegment the image with a smaller image unit than an ordinary object segment and well preserve the edges of the image. SLIC (Simple linear iterative clustering) is known for outperforming the previous superpixel methods with high image segmentation quality. Although the input parameter for SLIC, number of superpixels has considerable influence on image segmentation results, impact analysis for SLIC parameter has not been investigated enough. In this study, we performed optimal parameter analysis and evaluation of change detection for SLIC-based superpixel techniques using KOMPSAT data. Forsuperpixel generation, three superpixel methods (SLIC; SLIC0, zero parameter version of SLIC; SNIC, simple non-iterative clustering) were used with superpixel sizes in ranges of $5{\times}5$ (pixels) to $50{\times}50$ (pixels). Then, the image segmentation results were analyzed for how well they preserve the edges of the change detection reference data. Based on the optimal parameter analysis, image segmentation boundaries were obtained from difference image of the bi-temporal images. Then, DBSCAN (Density-based spatial clustering of applications with noise) was applied to cluster the superpixels to a certain size of objects for change detection. The changes of features were detected for each superpixel and compared with reference data for evaluation. From the change detection results, it proved that better change detection can be achieved even with bigger superpixel size if the superpixels were generated with high regularity of size and shape.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.155-158
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• 2019

최근 지능형 CCTV 관제 시스템에 대한 수요가 증가하고 있다. CCTV 영상 데이터의 양이 폭발적으로 증가하고 있어 이를 분석하기 위한 기술의 발전이 필요한 실정이다. 대부분의 지능형 CCTV 관제 시스템은 영상 속 객체를 찾고 이 객체의 메타데이터를 통해 지능형 관제 시스템을 수행한다. 하지만 영상 속 객체의 로그가 항상 정확하지 않다. 현재의 객체 인식 기술로는 CCTV 영상의 밝기, 해상도 조건에 따라 성능의 차이가 심하고, 영상의 프레임 대비 빠르게 움직인 CCTV 영상 속 모든 객체를 사람이 인식하는 정도로 인식하기 어렵다. 이러한 이동 객체의 크기, 위치를 분석한 메타데이터에는 에러가 포함되기 쉽다. 본 논문에서는 지능형 CCTV 관제 시스템에서 분석한 영상 속 객체의 프레임 메타데이터 에러를 학습기반 실시간 에러 필터링 알고리즘을 통해 개선하여 에러가 필터링된 데이터를 사용하는 지능형 관제 시스템의 정확도 향상에 기여 할 것을 기대한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.88-90
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• 2022

Semi Supervised Learning 은 일부의 data 에는 labeling 을 하고 나머지 data 에는 labeling 을 안한채로 학습을 진행하는 방법이다. Object Detection 은 이미지에서 여러개의 객체들의 대한 위치를 여러개의 바운딩 박스로 지정해서 찾는 Computer Vision task 이다. 당연하게도, model training 단계에서 사용되는 data set 의 크기가 크고 객체가 많을 수록 일반적으로 model 의 성능이 좋아 질 것이다. 하지만 실험 환경에 따라 data set 을 잘 확보하지 못하던가, 실험 장치가 데이터 셋을 감당하지 못하는 등의 문제가 발생 할 수 있다. 그렇기에 본 논문에서는 semi supervised learning based object detection model 을 알아보고 data set 의 크기를 조절해가며 modle 을 training 시킨 뒤 data set 의 크기에 따라 성능이 어떻게 변화하는 지를 알아 볼 것이다.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.819-829
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• 2019

In the disaster of collapse, an immediate response is needed to prevent the damage from worsening, and damage area calculation, response and recovery plan should be established. This requires accurate detection of the damage affected area. This study performed the detection of the damaged area by using UAV which can respond quickly and in real-time to detect the collapse accident. The study area was selected as B-05 housing redevelopment area in Jung-gu, Ulsan, where the demolition of houses and apartments in progress as the redevelopment project began. This area resembles a collapsed state of the building, which clear changes before and after the demolition. UAV images were acquired on May 17 and July 9, 2019, respectively. The changing area was considered as the damaged area before and after the collapse of the building, and the changing area was detected using CVA (Change Vector Analysis) the Representative Change Detection Technique, and SLIC (Simple Linear Iterative Clustering) based superpixel algorithm. In order to accurately perform the detection of the damaged area, the uninterested area (vegetation) was firstly removed using ExG (Excess Green), Among the objects that were detected by change, objects that had been falsely detected by area were finally removed by calculating the minimum area. As a result, the accuracy of the detection of damaged areas was 95.39%. In the future, it is expected to be used for various data such as response and recovery measures for collapse accidents and damage calculation.

• Electronics and Telecommunications Trends
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• v.27 no.4
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• pp.114-122
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• 2012

최근 CCTV(Closed Circuit Television)의 설치가 증가하면서 효율적인 모니터링을 위하여 지능형 영상분석 기술에 대한 관심이 높아지고 있다. 지능형 영상분석 기술은 영상의 정보를 분석하여 자동으로 이상 행위를 탐지하고 관리자에게 경보를 전송하는 기술로써, 사고를 사전에 예방하고 사고가 발생한 경우에는 신속하게 대응하여 피해를 줄일 수 있게 해준다. 본고에서는 지능형 영상분석 기술이 탐지할 수 있는 이상 행위, 즉 이벤트를 그 목적에 따라서 보안, 비즈니스 인텔리전스, 객체인식으로 구분하여 현재 기술 수준을 살펴볼 것이다. 그리고 앞으로 지능형 영상분석에서 이벤트 탐지 기술의 발전 방향을 사람의 행동인식, 행위 기반 이상 현상 탐지, 군중 환경에서 이벤트 탐지, 지능형 영상분석 구조의 변화 등의 관점으로 구분하여 살펴보고자 한다.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.265-278
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• 2019

Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data help to enable rapid emergency responses through detection of the artificial and natural disasters occurring at night. The DNB data without correction of lunar irradiance effect distributed by Korea Ocean Science Center (KOSC) has advantage for rapid change detection because of direct receiving. In this study, radiance differences according to the phase of the moon was analyzed for urban and mountain areas in Korean Peninsula using the DNB data directly receiving to KOSC. Lunar irradiance correction algorithm was proposed for the change detection. Relative correction was performed by regression analysis between the selected pixels considering the land cover classification in the reference DNB image during the new moon and the input DNB image. As a result of daily difference image analysis, the brightness value change in urban area and mountain area was ${\pm}30$ radiance and below ${\pm}1$ radiance respectively. The object based change detection was performed after the extraction of the main object of interest based on the average image of time series data in order to reduce the matching and geometric error between DNB images. The changes in brightness occurring in mountainous areas were effectively detected after the calibration of lunar irradiance effect, and it showed that the developed technology could be used for real time change detection.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.233-247
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• 2023

Recently, research on detecting objects in hidden spaces beyond the direct line-of-sight of observers has received attention. Most studies use optical equipment that utilizes the directional of light, but sound that has both diffraction and directional is also suitable for non-line-of-sight(NLOS) research. In this paper, we propose a novel method of detecting objects in non-line-of-sight (NLOS) areas using acoustic signals in the audible frequency range. We developed a deep learning model that extracts information from the NLOS area by inputting only acoustic signals and predicts the properties and location of hidden objects. Additionally, for the training and evaluation of the deep learning model, we collected data by varying the signal transmission and reception location for a total of 11 objects. We show that the deep learning model demonstrates outstanding performance in detecting objects in the NLOS area using acoustic signals. We observed that the performance decreases as the distance between the signal collection location and the reflecting wall, and the performance improves through the combination of signals collected from multiple locations. Finally, we propose the optimal conditions for detecting objects in the NLOS area using acoustic signals.