• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.504-509
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• 2019

Recently, both wireless communications technology and the performance of small devices have developed exponentially, while the number of services using various types of Internet of Things (IoT) devices has also massively increased in line with the ongoing technological and environmental changes. Furthermore, ever more devices that were previously used in the offline environment-including small-size sensors and CCTV-are being connected to the Internet due to the huge increase in IoT services. However, many IoT devices are not equipped with security functions, and use vulnerable open source software as it is. In addition, conventional network equipment, such as switches and gateways, operates with vulnerabilities, because users tend not to update the equipment on a regular basis. Recently, the simple vulnerability of IoT devices has been exploited through the distributed denial of service (DDoS) from attackers creating a large number of botnets. This paper proposes a system that is capable of identifying Internet-connected devices quickly, analyzing and managing the vulnerability of such devices using Internet-wide scan technology. In addition, the vulnerability analysis rate of the proposed technology was verified through collected banner information. In the future, the company plans to automate and upgrade the proposed system so that it can be used as a technology to prevent cyber attacks.

• Journal of the Society of Disaster Information
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• v.15 no.3
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• pp.358-366
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• 2019

Purpose: This paper aims to clarify the problems and to examine the improvement methods by investigating the management condition of local-designated cultural property of which management is relatively poor in comparison with state-designated cultural heritage. Method: In order to grasp the management situation of the local-designated cultural heritage, a research on cultural heritage management situation and problems will be carried out with 35 cultual heritages in Goryeong-gun. Also, the improvement methods about the property type vulnerability on the basis of interview with cultual property managers, fire-fighting officers and civil servants, etc. Results: Local cultural heritages were investigated to be very vulnerable to the fire of wooden buildings, the theft of movable cultural heritages, and the effects of wind and water damage. It is because cultural heritages are scattered over wide areas fundamentally. As the result, it has difficulty in the patrols of police officers and fire fighters, and in the situation that it lacks disaster monitoring and CCTV for countermeasures to replace them, electronic security including fire hydrant, sensors, etc and fire extinguishing facilities and so on. It is difficult for local governments managing local-designated cultural heritages to enhance their management systems directly due to their lack of budget and manpower. Conclusion: In order to strengthen disaster and safety management system for the cultural heritages designated by local governments, they have to clarify disaster countermeasure task of fire fighting, police, and cultural heritage managers prepare their manuals, and systematize them through disaster drill mainly in local autonomous governments. Also, so as to establish a surveillance system every day, they have to enhance the community for local cultural heritage manage consisting of local volunteer fire departments, local voluntary disaster prevention organizations, volunteers, etc.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.161-168
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• 2019

Recently, video equipments such as CCTV, which is spreading rapidly, is being used as a means to monitor and cope with abnormal situations in almost governments, companies, and households. However, in most cases, since recognizing the abnormal situation is carried out by the monitoring person, the immediate response is difficult and is used only for post-analysis. In this paper, we present the results of the development of video surveillance system that automatically recognizing the abnormal situations and sending such events to the smartphone immediately using the latest deep learning technology. The proposed system extracts skeletons from the human objects in real time using Openpose library and then recognizes the human behaviors automatically using deep learning technology. To this end, we reconstruct Openpose library, which developed in the Caffe framework, on Darknet framework to improve real-time processing. We also verified the performance improvement through experiments. The system to be introduced in this paper has accurate and fast behavioral recognition performance and scalability, so it is expected that it can be used for video surveillance systems for various applications.

• Journal of the Korean Society of Mineral and Energy Resources Engineers
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• v.55 no.6
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• pp.576-587
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• 2018

This study has focused on analysis factors affecting safety monitoring system at tailings sites, and the evaluation equipment to monitor the factors. Twenty sites at eighteen mines with unsafe conditions were selected to examine the equipment. There were three main factors influenced safety in the sites including surface erosion, piping, and slope instability. In detail, the surface erosion was divided into three sub-factors (planting, soil-topping layer, and tailings), piping into three sub-factors (liner, rain protection facility and leachate), and slop instability was also divided into three sub-factors (slop, concrete wall, and reinforcing wall). As results of in-field measurement, a CCTV was the most effective facility, and electrical resistivity survey, acoustic sensing, thermal liner sensor, structure inclinometer, rainfall meter, and flowmeter were also highly effective. According to applications of the facilities in the unstable tailings, structural defects were mainly found in the piping, which was the most important monitoring factor for safety management of tailings sites.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.177-188
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• 2019

Recently, citizens are feeling anxious as 'Motiveless Crime' increases. The quality of citizens life is degraded and the degree of crime fear is increasing. In this study, based on various variables related to crime other than actual crime occurrence status, crime occurrence points (point line polygon) felt by citizens are created by using mental map methodology. And the purpose of this study is to derive the area of concern for crime through spatial overlap analysis using kernel density estimation analysis. It also uses spatial overlay analysis using kernel density estimation to derive areas of concern for crime occurrence. As a result, the local residents' request point and the areas of concern for crime were overlapped. In addition, the mental map indicating the fear of crime was constructed by mapping mainly the areas between the facilities, the non-construction area such as the narrow area, the security CCTV, the streetlight. This study is meaningful in that it tried to derive a crime occurrence concern area by using mental map method unlike the previous study related to crime. The results of this study, such as mental map, could be used in various fields such as construction of fragile crime map, guideline of crime prevention through environment design.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.85-91
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• 2020

Recently, video-taping equipment such as CCTV have been seeing more use for crime prevention and general safety concerns. Since these video-taping equipment operates all throughout the day, the need for security personnel is lessened, and naturally costs incurred from managing such manpower should also decrease. However, technology currently used predominantly lacks self-sufficiency when given the task of searching for a specific object in the recorded video such as a person, and has to be done manually; current security-based video equipment is insufficient in an environment where real-time information retrieval is required. In this paper, we propose a technology that uses the latest deep-learning technology and OpenCV library to quickly search for a specific person in a video; the search is based on the clothing information that is inputted by the user and transmits the result in real time. We implemented our system to automatically recognize specific human objects in real time by using the YOLO library, whilst deep learning technology is used to classify human clothes into top/bottom clothes. Colors are also detected through the OpenCV library which are then all combined to identify the requested object. The system presented in this paper not only accurately and quickly recognizes a person object with a specific clothing, but also has a potential extensibility that can be used for other types of object recognition in a video surveillance system for various purposes.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.45-51
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• 2022

Fear of contact exists due to the prevention of the spread of infectious diseases such as COVID-19. When using the common entrance door of an apartment, access is possible only if the resident enters a password or obtains the resident's permission. There is the inconvenience of having to manually enter the number and password for the common entrance door to enter. Also, contactless entry is required due to COVID-19. Due to the development of ICT, users can be easily identified through the development of face recognition and voice recognition technology. The proposed method detects a visitor's face through a CCTV or camera attached to the common entrance door, recognizes the face, and identifies it as a registered resident. Then, based on the registered information of the resident, it is possible to operate without contact by interworking with the elevator on the server. In particular, if face recognition fails with a hat or mask, the visitor is identified by voice or additional authentication of the visitor is performed based on the voice message. It is possible to block the spread of contagiousness without leaving any contactless function and fingerprint information when entering and exiting the front door of an apartment house, and without the inconvenience of access.