• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.434-436
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• 2022

For 4th industrial revolution and the development of various communication media, unmanned and automation are rapidly progressing in various fields. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. Accordingly, the importance of preprocessing in a system operating based on an image is increasing, and an algorithm for effectively removing noise from an image is attracting attention. In this paper, we propose a filtering algorithm using noise judgment and a segmentation mask in a complex noise environment. The proposed algorithm calculates the final output by switching the segmentation mask suitable for filtering by performing noise judgment on the pixel values of the input image. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared and evaluated with the existing filter algorithm.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.1-6
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• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.2
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• pp.98-103
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• 2010

In this paper, imaging processing study obtained from CCD image and thermography image was performed in order to treat easily thermographic data without any risks of personnels who conduct the condition monitoring for the abnormal or failure status occurrable in industrial power plants. This imaging processing is also applicable to the predictive maintenance. For confirming the broad monitoring, a methodology producting single image from the panorama technique was developed no matter how many cameras are employed, including fusion method for discrete configuration for the target. As results, image fusion from quick realtime processing was obtained and it was possible to save time to track the location monitoring in matching the images between CCTV and thermography.

• 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 Korea Information Processing Society Conference
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• 2007.11a
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• pp.47-49
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• 2007

교통정보수집 시스템이란 CCTV나 웹캠을 통해 얻어진 영상을 토대로 차선별, 혹은 주행방향별 교통량과 통과 차량들의 속도를 실시간으로 측정하는 시스템이다. 차선별로 각각 두 개의 라인을 설정하고 이를 이용하여 차선별 속도와 교통량을 측정한다. 이 때 차선별로 설정된 두 라인에 해당하는 영역에 대해서 배경 값을 지속적으로 갱신한다. CCTV와 웹캠을 이용하여 수집한 영상을 실험에 사용한 결과 평균 86.2%의 차선별 주행차량 검지율을 보였으며, 검지된 차량들을 차선별 방향별로 구분하여 평균 속도를 측정하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2430-2436
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• 2013

This study suggests a method of detecting the still object, which becomes a cause of danger within the crossroad. The Inverse Perspective Transform was performed in order to make the object size consistent by being inputted the real-time image from CCTV that is installed within the crossroad. It established the detection area in the image with the perspective transform and generated the adaptative background image with the use of the moving information on object. The detection of the stop object was detected the candidate region of the stop object by using the background-image differential method. To grasp the appearance of truth on the detected candidate region, a method is proposed that uses the gradient information on image and EHD(Edge Histogram Descriptor). To examine performance of the suggested algorithm, it experimented by storing the images in the commuting time and the daytime through DVR, which is installed on the cross street. As a result of experiment, it could efficiently detect the stop vehicle within the detection region inside the crossroad. The processing speed is shown in 13~18 frame per second according to the area of the detection region, thereby being judged to likely have no problem about the real-time processing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2355-2362
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• 2016

Seg fog removal is an important issue concerned by both computer vision and image processing. Sea fog or haze removal is widely used in lots of fields, such as automatic control system, CCTV, and image recognition. Color image dehazing techniques have been extensively studied, and expecially the dark channel prior(DCP) technique has been widely used. This paper propose a fast and efficient image prior - dark channel prior to remove seg-fog from a single digital image based on the GPU. We implement the basic parallel program and then optimize it to obtain performance acceleration with more than 250 times. While paralleling and the optimizing the algorithm, we improve some parts of the original serial program or basic parallel program according to the characteristics of several steps. The proposed GPU programming algorithm and implementation results may be used with advantages as pre-processing in many systems, such as safe navigation for ship, topographical survey, intelligent vehicles, etc.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.206-211
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• 2011

In this paper, a fire detection algorithm based on video processing is proposed. At the first stage, background image extracted from CCTV video input signal, and then foreground image were separated by differencing CCTV input signal from background image. At the second stage, candidated area were extracted by using color information from foreground image. At the final stage, smoke or flame characteristic area were separated by using Gaussian mixture modeling applied to candidated area, and then fire can be detected. Through real experiments at the inner room, it is shown that the proposed system works well.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.169-177
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• 2014

It is essential to support the image and video analysis technology such as motion detection since the DVR and NVR storage were adopted in the real time visual surveillance system. Especially the network camera would be popular as a video input device. The traditional CCTV that supports analog video data get be replaced by the network camera. In this paper, we present the design and implementation of video surveillance system that provides the real time motion detection by the video storage server. The mobile application also has been implemented in order to provides the retrieval functionality of image analysis results. We develop the video analysis server with open source library OpenCV and implement the daemon process for video input processing and real-time image analysis in our video surveillance system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.133-135
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• 2022

With the development of IoT and AI technologies and media, various digital devices are being used, and unmanned and automation is progressing rapidly. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. However, noise present in the image affects processes such as edge detection and object recognition, and causes deterioration of system accuracy and reliability. In this paper, we propose a filtering algorithm using fuzzy weights to reconstruct images damaged by complex noise. The proposed algorithm obtains a reference value using noise judgment and calculates the final output by applying a fuzzy weight. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared with the existing filter algorithm and evaluated.