• Journal of Digital Convergence
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• v.13 no.9
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• pp.183-189
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• 2015

In this paper, we propose an object tracking method using the color information of the image in surveillance network. This method perform a object detection using of adaptive color model. Object contour detection plays an important role in application such as object recognition. Experimental results demonstrate successful object detection over a wide range of object's variation in color and scale. In applications to detect an object in real time, when transmitting a large amount of image data it is possible to find the mode of a color distribution. The specific color of an object is modified at dynamically changing color in image. So, this algorithm detects the tracking area information of object within relevant tracking area and only tracking the movement of that object.Through experiments, we show that proposed method is more robust than other methods under certain ideal situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.5
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• pp.35-49
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• 2014

This paper proposes the on-road object detection and classification algorithm by using a detection system consisting of only laser scanners. Each sensor data acquired by the laser scanner is fused with a grid map and the measurement error and spot spaces are corrected using a labeling method and dilation operation. Fuzzy method which uses the object information (length, width) as input parameters can classify the objects such as a pedestrian, bicycle and vehicle. In this way, the accuracy of the detection system is increased. Through experiments for some scenarios in the real road environment, the performance of the proposed detection and classification system for the actual objects is demonstrated through the comparison with the actual information acquired by GPS-RTK.

• 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.

• Journal of Internet Computing and Services
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• v.5 no.5
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• pp.93-103
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• 2004

The main function of the intrusion Detection System (IDS) usee to be more or less passive detection of the intrusion evidences, but recently it is developed with more diverse types and methodologies. Especially, it is required that the IDS should process large system audit data fast enough. Therefore the data mining or neural net algorithm is being focused on, since they could satisfy those situations. In this study, we first surveyed and analyzed the several recent intrusion trends and types. And then we designed and implemented an IDS using back-propagation algorithm of the neural net, which could provide more effective solution. The distinctive feature of our study could be stated as follows. First, we designed the system that allows both the Anomaly dection and the Misuse detection. Second, we carried out the intrusion analysis experiment by using the reliable KDD Cup ‘99 data, which would provide us similar results compared to the real data. Finally, we designed the system based on the object-oriented concept, which could adapt to the other algorithms easily.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1125-1136
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• 2010

Lately there exist increasing demands for online abnormality monitoring over trajectory stream, which are obtained from moving object tracking devices. This problem is challenging due to the requirement of high speed data processing within limited space cost. In this paper, we present a FADA (Fuzzy Anomaly Detection Algorithm) which constructs normal profile by computing mobility feature information from the GPS (Global Positioning System) logs of mobile devices in MANETs (Mobile Ad-hoc Networks), computes a fuzzy dissimilarity between the current mobility feature information of the mobile device and the mobility feature information in the normal profile, and detects effectively the anomaly behaviors of mobile devices on the basis of the computed fuzzy dissimilarity. The performance of proposed FADA is evaluated through simulation.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.717-720
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• 2005

감시, 인지, 보안 시스템으로부터 얻은 비디오 영상에서 원하는 객체를 탐지해 내는 것은 매우 중요하다. 객체 추출 방법은 여러 가지가 있지만 가장 많이 쓰이는 방법이 배경을 이용하는 방법이다. 이때 실외 환경에 설치된 카메라의 경우 날씨, 시간에 따른 태양의 밝기등과 영상 내의 객체의 변화 량에 따라서 효율적으로 적응할 수 배경 추출 알고리즘이 필요하다. 본 논문에서는 빠르고 정확하게 배경을 얻기 위한 기본적인 방법인 평균값과 최빈값을 이용한 방법을 혼합하여 영상의 변화 량에 따른 빠르고 정확한 배경을 추출하는 알고리즘을 제안하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.366-369
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• 2021

본 논문에서는 동영상에서 일련의 과정을 거쳐 얻었던 학습데이터를 보다 간편하고 빠른 속도로 획득하는 방법을 제안한다. 음성과 영상 스트림을 처리하는 ffmpeg을 이용해 영상을 프레임화하고, 딥 러닝 기반의 YOLO 알고리즘을 사용하여 객체를 검출한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.91-98
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• 2022

In this study, Pukyong National University's floating population survey and analysis were conducted using Raspberry Pie, an open source hardware, and object detection algorithms based on deep learning technology. After collecting images using Raspberry Pie, the person detection of the collected images using YOLO3's IMAGEAI and YOLOv5 models was performed, and Haar-like features and HOG models were used for accuracy comparison analysis. As a result of the analysis, the smallest floating population was observed due to the school anniversary. In general, the floating population at the entrance was larger than the floating population at the exit, and both the entrance and exit were found to be greatly affected by the school's anniversary and events.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.151-162
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• 2022

Recently, the forests in Korea have accumulated damage due to continuous forest disasters, and the need for technologies to monitor forest managements is being issued. The size of the affected area is large terrain, technologies using drones, artificial intelligence, and big data are being studied. In this study, a standard dataset were conducted to develop an algorithm that automatically detects suspicious trees damaged by forest pests using deep learning and drones. Experiments using the YOLO model among object detection algorithm models, the YOLOv4-P7 model showed the highest recall rate of 69.69% and precision of 69.15%. It was confirmed that YOLOv4-P7 should be used as an automatic detection algorithm model for trees suspected of being damaged by forest pests, considering the detection target is an ortho-image with a large image size.