• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.498-500
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• 2022

CCTV protects people and assets safely by identifying dangerous situations and responding promptly. However, it is difficult to continuously monitor the increasing number of CCTV images. For this reason, there is a need for a device that continuously monitors CCTV images and notifies when abnormal behavior occurs. Recently, many studies using artificial intelligence models for image data analysis have been conducted. This study simultaneously learns spatial and temporal characteristic information between image data to classify various abnormal behaviors that can be observed in CCTV images. As an artificial intelligence model used for learning, we propose a multi-classification deep learning model that combines an end-to-end 3D convolutional neural network(CNN) and ResNet.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.92-105
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• 2016

This study aims to establish a simulation method for CCTV (Closed Circuit Television) sight area. The simulation incorporates variables for computing CCTV sight area including CCTV specifications and installation. Currently CCTV is used for traffic, crime prevention and fire prevention by local governments. However, new locations are selected by administrator decision rather than analysis of the optimal location. In order to determine optimum location, a method to CCTV compute range is needed, which incorporates specifications according to CCTV purpose. For this purpose, limitations of previous research methods must be recognized and the simulation method must supplement these limitations. Here in this study, we derived CCTV sight area variables for realistic analysis to complement the limitations of previous studies. A total of eight elements were derived from image device sensors and installation: wide angle, height, angle, setting height, setting angle, and others. This research implemented a 3D simulation technique that can be applied to the derived factors and automate them using ArcObject and Visual C#. This simulation method can calculate sight range in accordance with CCTV specifications. Furthermore, when installing additional CCTVs, it can derive optimal allocation position. The results of this study will provide rational choices for specification selection and CCTV location by interagency collaborative projects.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.5
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• pp.789-795
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• 2011

This paper aims to develop authoring tools and services platform that can be Immediate response through supervisor's intuitive understanding based 3d-Building Information mode about overall security situation of building by mapping many CCTV images on 3D space information from traditional observation way that make simply visualize CCTV images on the Situation Board.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.4
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• pp.559-565
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• 2011

In this paper, we propose the geographic information systems that is able to build geographic information effectively by creating 3D topography after extraction surrounding terrain information through the video shooting in the CCTV camera. We also propose tracing method for object recognized through the video shooting of camera and recognition method which is whether or not the terrain change according to success or not of tracing the object. We apply this method in the industry field we can build a geographic information close to the actual terrain, but also can be used for security, surveillance and tracking system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.2
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• pp.153-162
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• 2014

As CCTVs are widely utilized in diverse fields, many researchers have continuously studied to improve the surveillance performances of a CCTV system. However, an quantitative evaluation approach about the surveillance performance has rarely been researched. Therefore, we set up the research for suggesting a quantitative evaluation approach to determine the effectiveness of CCTV coverages. We firstly defined the surveillance resolution as that varies according to object's positions and orientations. Based on the definition, we computed surveillance resolution values at all three-dimensional positions with the orientations of interests in the specified space. By comparing these values to the required reasonable resolution, we determined the surveillance performance index indicating how well a CCTV system monitor a target space for specific surveillance objectives. This proposed approach evaluates the surveillance performance of a CCTV system quantitatively, so as examines the CCTV system design before its installation based on precise 3D spatial analysis.

• Journal of Korean Ophthalmic Optics Society
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• v.13 no.1
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• pp.31-35
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• 2008

Purpose: This study is to design a triplet optical system for CCTV camera lens. Methods: It was a telescopic lens with $5^{\circ}$ field angle, 56 mm focal length, 20 mm diameter, and 2/3 inches sized CCD array detector. Results: The performance of the subject optical system was evaluated by applying ray fan, spot diagram, and diffraction optical MTF. The wavelength was achromatized at Fraunhofer C, d and F-line, and both MTF and tangential & sagittal MTF shows more than 70% at spatial frequency of 50 linepairs/mm. Conclusions: The marketable triplet optical system for CCTV camera was designed and its utility was considered.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.7-9
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• 2020

기존 CCTV 비디오에서 딥러닝 기반의 이상 탐지 연구는 객체의 행동 값만을 이용하여 이상을 탐지하였기 때문에, 시간 흐름에 따른 정보가 축소되는 문제점이 있었다. 그러나 CCTV 비디오에서의 이상의 원인은 다양한 요소와 시계열 분석에 따른 정보로 이루어져 있어 시간 정보를 유지하면서 다양한 특징 값을 사용한 모델을 설계할 필요가 있다. 따라서 본 논문에서는 C3D에 광학 흐름을 결합한 새로운 앙상블 모델을 제안한다. 실험 결과 본 논문에서 제안하는 모델이 75.83의 AUC를 얻어 기존에 연구되었던 행동 값만을 사용한 모델보다 높은 정확도를 달성하였다. 또한 이상 탐지 모델 설계 시 객체의 행동에 다양한 측면을 고려할 수 있는 여러 특징 값과 시계열 분석에 따른 정보를 사용하는 것이 적절하다는 결론을 도출하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.664-670
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• 2018

In modern society, CCTV, which is the eye of surveillance, is being used to collect image data in various ways in daily life. CCTV is used not only for security, surveillance, and crime prevention but also in many fields such as automobile and black box. In this paper, we have developed a STM32F407 ARM chip based camera system for various applications. In order to develop camera system, modeling of camera system based on 3D structure was carried out in SolidWorks environment. The PCB board design was developed to extract the PCB parts from the camera system modeling files into iges files, convert them from the Altium Designer tool into 3D and 2D boards, After designing the camera system circuit and PCB, we have been studying the implementation of the stable system by using TRM (Thermal Risk Management) tool to cope with the heat simulation generated on the board.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.95-103
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• 2009

Global interests for construction of 3 dimensional spatial information has risen due to development of measurement sensors and data processing technologies. In spite of criticism for the violation of personal privacy, CCTV cameras equipped in outdoor public space of urban area are used as a fundamental sensor for traffic management, crime prevention or hazard monitoring. For safety guarantee in urban environment and disaster prevention, a surveillance system integrating pre-constructed 3 dimensional spatial information with CCTV data or video sequence is needed for monitoring and observing emergent situation interactively in real time. In this study, we proposed applicability of the prototype system for web visualization based on X3D, an international standard of real time web visualization, by integrating 3 dimensional spatial information with video sequence.

• 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.