• Journal of IKEEE
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• v.20 no.2
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• pp.143-151
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• 2016

Smart care surveillance system can take the position information of the user and monitor by controlling neighbor CCTVs using GPS receiver built into smart device. But because the position information contains a significant error, in general smart device, it is necessary to be corrected for precise monitoring. In previous smart care system, this error is corrected by using a plurality of CCTV. But it has disadvantage that two or more CCTVs pointed toward the same point at the same time. In this paper, we propose the method to correct error of the GPS location information by using only one CCTV. With experiment result, we find that the accuracy of GPS location information corrected with only one CCTV is as improved as two CCTVs.

• Journal of IKEEE
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• v.22 no.2
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• pp.266-273
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• 2018

Smart surveillance system obtains the positional information of users using GPS receivers embedded in smart devices, and provide them with services of tracking, monitoring, and protecting by controlling CCTVs nearby them. In order to apply and operate these systems to new environment, the overall setup process of the system is increased proportionally to the number of CCTVs. Therefore if there is a large number of CCTVs, the amount of time required during the setup process is very lengthy, and the operation of them becomes inoperable. In this paper, we propose a method to reduce these setting process. As the result of applying and simulation the proposed method, the setup method is simple, and as the CCTV increases, it consumes less time the previous system, and the system can be operated during setup.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.97-106
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• 2023

Citizens' demand for public CCTVs continues to rise, along with an increase in variouscrimes and social problems in cities. In line with the needs of citizens, the Seoul Metropolitan Government began installing CCTV cameras in 2010, and the number of new installations has increased by over 10% each year. As the large surveillance system represents a substantial budget item for the city, decision-making on location selection should be guided by reasonable standards. The purpose of this study is to improve the existing related models(such as public CCTV priority location analysis manuals) to establish the methodology foranalyzing priority regions ofSeoul-type public CCTVs and propose new mid- to long-term installation goals. Additionally, using the improved methodology, we determine the CCTV priority status of 25 autonomous districts across Seoul and calculate the goals. Through its results, this study suggests improvements to existing models by addressing their limitations, such as the sustainability of input data, the conversion of existing general-purpose models to urban models, and the expansion of basic local government-level models to metropolitan government levels. The results can also be applied to other metropolitan areas and are used by the Seoul Metropolitan Government in its CCTV operation policy

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.75-80
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• 2017

Recently, there has been growing interest in spatial data that combines information and communication technology with smart cities. The high-precision LiDAR (Light Dectection and Ranging) equipment is mainly used to collect three-dimensional spatial data, and the acquired data is also used to model geographic features and to manage plant construction and cultural heritages which require precision. The LiDAR equipment can collect precise data, but also has limitations because they are expensive and take long time to collect data. On the other hand, in the field of computer vision, research is being conducted on the methods of acquiring image data and performing 3D reconstruction based on image data without expensive equipment. Thus, precise 3D spatial data can be constructed efficiently by collecting and processing image data using CCTVs which are installed as infrastructure facilities in smart cities. However, this method can have an accuracy problem compared to the existing equipment. In this study, experiments were conducted and the results were analyzed to increase the number of extracted matching points by applying the feature-based method and the area-based method in order to improve the precision of 3D spatial data built with image data acquired from stereo CCTVs. For techniques to extract matching points, SIFT algorithm and PATCH algorithm were used. If precise 3D reconstruction is possible using the image data from stereo CCTVs, it will be possible to collect 3D spatial data with low-cost equipment and to collect and build data in real time because image data can be easily acquired through the Web from smart-phones and drones.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.130-132
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• 2008

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• Journal of Digital Contents Society
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• v.14 no.4
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• pp.447-454
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• 2013

As the surveillance CCTVs are increasing every year, big data image processing for the CCTV image data has become a hot issue. In this paper, we propose a Hadoop-based big data image processing technique to recognize a vehicle number from a large amount of automatic number plate images taken from CCTVs. We also implement the vehicle route tracking system that displays the moving path of the searched vehicle on Google Maps with the related information together. In order to evaluate the performance we compare and analysis the vehicle number recognition time for a lot of CCTV image data in Hadoop and the single PC environment.

• Convergence Security Journal
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• v.17 no.5
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• pp.49-55
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• 2017

In case of security of IoT-based areas in ICBM industry, the anxiety about safety goes to extremes in public and national safety area, so that the demand for security service related to disaster/safety management is increasing. Like this, as a security service for safety, CCTVs are installed/operated for the purpose of maintenance of public order and crime prevention. Especially, as the recorded images are presented as crucial evidences of crimes, they are rapidly increasing. However, as adverse effects of CCTVs, it is highly possible to unintentionally leak personal information in the process of performing the original purpose, or to violate someone's privacy in case when such technologies are abused. Therefore, it would be necessary to have researches on the multilaterally-combined mechanism for the protection of image privacy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.346-348
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• 2022

Many CCTVs are needed to monitor a physically large area. When installing CCTV indoors, the wiring environment is sufficient, so many CCTVs can be installed. However, wiring is relatively difficult outdoors. In addition, when monitoring a long distance, wiring costs to the monitoring site are incurred. Therefore, when installing CCTV at a physically long distance, it is necessary to apply wireless technology. In this study, the structure of the existing CCTV system was checked and the requirements for converting it to a wireless environment were derived. And according to the requirements, a wireless-based intelligent CCTV system was proposed. As a result, it was confirmed that the wireless-based intelligent CCTV proposed in this study operates normally in a wireless environment. This study was conducted based on the wifi environment, and additional research is needed to extend it to the mobile mobile telecommunication environment.

• Smart Structures and Systems
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• v.30 no.5
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• pp.521-535
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• 2022

Efficient management of deteriorating civil infrastructure is one of the most important research topics in many developed countries. In particular, the remote displacement measurement of bridges using linear variable differential transformers, global positioning systems, laser Doppler vibrometers, and computer vision technologies has been attempted extensively. This paper proposes a remote displacement measurement system using closed-circuit televisions (CCTVs) and a computer-vision-based method for in-situ bridge bearings having relatively large displacement due to temperature change in long term. The hardware of the system is composed of a reference target for displacement measurement, a CCTV to capture target images, a gateway to transmit images via a mobile network, and a central server to store and process transmitted images. The usage of CCTV capable of night vision capture and wireless data communication enable long-term 24-hour monitoring on wide range of bridge area. The computer vision algorithm to estimate displacement from the images involves image preprocessing for enhancing the circular features of the target, circular Hough transformation for detecting circles on the target in the whole field-of-view (FOV), and homography transformation for converting the movement of the target in the images into an actual expansion displacement. The simple target design and robust circle detection algorithm help to measure displacement using target images where the targets are far apart from each other. The proposed system is installed at the Tancheon Overpass located in Seoul, and field experiments are performed to evaluate the accuracy of circle detection and displacement measurements. The circle detection accuracy is evaluated using 28,542 images captured from 71 CCTVs installed at the testbed, and only 48 images (0.168%) fail to detect the circles on the target because of subpar imaging conditions. The accuracy of displacement measurement is evaluated using images captured for 17 days from three CCTVs; the average and root-mean-square errors are 0.10 and 0.131 mm, respectively, compared with a similar displacement measurement. The long-term operation of the system, as evaluated using 8-month data, shows high accuracy and stability of the proposed system.