• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.2
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• pp.69-75
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• 1985

The proposed seal identification procedure consists of the smoothing, rotation, thinning, and matching techniques. The seal images which are scanned by CCTV are thresholded into the binary prctures of $256{\times}256$ pixels through A/D converter and 6502 microcomputer. After the sample and target images are ratated into an identical orientation, a thinning process is used to extract the skeletons of the character strobes. The wighted map is constructed by distance weight from which the distance weighted correlation C is computed. The C is compared with the dicision constant C or C for the purpose of seal indentification. The identification rate is 95% and the total CPU time is less than 3 minutes for each identification in the experiment.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.265-273
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• 2008

Video Image Processing (VIP) for traffic surveillance has been used not only to gather traffic information, but also to detect traffic conflicts and incident conditions. This paper presents a system development of gathering traffic information and conflict detection based on automatic calculation of pixel length within the detection zone on a Video Detection System (VDS). This algorithm improves the accuracy of traffic information using the automatic detailed line segmentsin the detection zone. This system also can be applied for all types of intersections. The experiments have been conducted with CCTV images, installed at a Bundang intersection, and verified through comparison with a commercial VDS product.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.376-378
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• 2022

Artificial intelligence technology is widely used in various fields such as artificial intelligence speakers, artificial intelligence chatbots, and autonomous vehicles. Among these AI application fields, the image processing field shows various uses such as detecting objects or recognizing objects using artificial intelligence. In this paper, data synthesized by a virtual human is used as a method to analyze images taken in a specific space.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05b
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• pp.1477-1480
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• 2003

본 논문에서는 실시간 데이터 및 보안데이터, 영상데이터 통을 전송할 때 잡음으로 인해 발생되는 데이터 오류를 효과적으로 복원하기 위해 오류 정정 능력이 뛰어난 터보코드를 적응하여 신뢰성 있는 영상전송 시스템을 실현하였다. 영상처리 시스템에서는 CCTV, 비디오 카메라 등에서 나오는 NTSC(National Television System Committee) 영상 신호를 비디오 디코더를 통해 A/D 변환하여 출력하였다. 변환된 디지털 영상정보는 두 개의 영상필드로 출력되며 그중 하나의 필드가 선택되는 알고리즘을 EPLD(Erasable Programmable Logic Device) 로직회로로 구성하여 디지털 영상 데이터를 절반으로 줄이는 시스템을 구현하였다. 터보코드의 부호기, 복호기 시스템에서는 실수연산이 가능한 DSP(Digital Signal Processor)를 사용하여 터보코드를 구현하였으며, 터보코드의 성능을 좌우하는 인터리버부분은 블록 인터리버를 적용하여 설계하였다.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.290-293
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• 1988

This paper shows the development of robot sensor system using optical fiber. When the object recognition is implemented in the existing robot system, the image processing method using CCTV-Camera has been most widely used. But when it is necessary to recognize and classify only simple elements in industrial field, the real time processing using this method requires relatively high cost. The purpose of this paper is to represent the fundamental study on the development of optical-fiber sensor system, which may be used to recognize and classify elements with low cost and real-time.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1016-1018
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• 2019

흑백 1채널 이미지를 3 채널 이미지로 색상화하고 Super-Resolution하여 의미 있는 정보 얻도록 한다. CCTV, 군사용 카메라, 차량용 블랙박스 등 많은 분야에서 주간에 촬영된 영상은 컬러 이미지로 많은 정보를 얻을 수 있다. 하지만 야간에 촬영된 영상은 빛이 없어서 영상에서 정보를 얻기가 원활하지 않다. 따라서 DCGAN을 통해 단일 채널의 흑백 이미지를 3채널의 색상화 이미지로 만들고, Super-Resolution 기술을 적용해서 해상도를 높여 가시광선이 없는 야간이나 어두운 공간에서도 의미있는 영상을 얻을 수 있도록 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.470-473
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• 2021

외부인들이 시설을 무단으로 이용하는 등의 범죄가 계속해서 발생하고 있다. 본 논문은 기존의 시설물에서 사용하고 있는 단순 인증 절차가 아닌 BLE 비콘과 영상데이터를 활용한 비승인자 감지 시스템이다. 이 시스템은 스마트폰 어플리케이션에서 BLE 비콘의 데이터를 받은 후 UUID 값과 RSSI 값을 서버로 전송한다. 이후 전송된 데이터들로 핑거프린팅 기반 RadioMap을 구성하고 RNN 기반 딥러닝 학습을 진행하여 사용자 위치 데이터를 도출한다. CCTV를 통해 수집된 영상데이터는 서버로 전송되며, YOLOv4를 이용하여 객체탐지를 위한 프로세스를 진행한 후 Person 클래스를 추출한다. 이후 승인된 사용자의 위치 데이터에 실시간 영상데이터를 더하여 인증 과정 절차가 진행되지 않은 비승인자들을 추적한다. 본 논문은 COVID-19로 인해 시설물 인증 절차에 사용이 증가하고 있는 QR코드를 이용해 인증 과정 절차의 진행 방식으로 시스템에 대한 확장성까지 기대할 수 있다.

• Journal of the Korea Convergence Society
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• v.7 no.5
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• pp.29-34
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• 2016

Recently, with the development of high-tech industry, the use of the drones in various aspects of daily life is rapidly advancing. With technical and functional advancements, drones have an advantage of being easy to be utilized in the areas of use according to various lifestyles. In addition, through the diversification of the drone service converged with image processing medium such as camera and CCTV, an automated security system that can replace humans is expected to be introduced. By designing these unmanned security technology, a new convergence security drone service techniques that can strengthen the previous drone application technology will be proposed. In the proposed techniques, a biometric authentication technology will be designed as additional authentication methods that can determine the safety incorporated with security by selecting the search and areas of an object focusing on the objects in the initial windows and search windows through OpenCV technology and CAM-Shift algorithm which are an object tracking algorithm. Through such, a highly efficient security drone convergence service model will be proposed for performing unmanned security by using the drones that can continuously increase the analysis of technology on the mobility and real-time image processing.

• Computers and Concrete
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• v.33 no.4
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• pp.425-443
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• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.