• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.83-90
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• 2010

In modern society, there has been an increase in needs to protect the life and property of the people, because the number of various crimes is on the increase due to the sudden and complicated changes of the urban environment. For the needs, security persons in the urban area are expanding the role and skill of police for more effective crime prevention and surveillance, although the number of policeman/woman is insufficient and their tasks are hard. Recently, a system to observe and prevent crime in effective has been introduced by using such an artificial surveillance device as CCTV to monitor focusing on one area for 24 hours. However, the system brings such problems as the insufficiency of systematic criteria to install surveillance device and the invasion of privacy. Therefore, in this study, artificial surveillance zones to monitor crimes are selected by applying spatial features between artificial surveillance devices including CCTV and crime occurrence place, and using GIS spatial analysis techniques. As a result of selecting, it's found that the number of CCTV is absolutely insufficient and spatial distribution is not fully considered in the existing location of installed CCTV.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.

• Journal of the Society of Disaster Information
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• v.15 no.1
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• pp.153-164
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• 2019

Purpose: This paper aims to derive improvement measures, in terms of legal and technical aspects, which can reduce effectively the casualties caused by drowing accidents. Method: Firstly, we checked the status of drowing accident management and carried out the interview of field private safety guards. field private safety guards. In addition, surveys were conducted on safety personnel and managers. Based on survey results, we are lastly analyzed the specific problems and reviews the improvement measures from technical and legal aspects. Result: As an analytical result, it was considered that supplementary supporting tools such as CCTV, monitoring devices using IoT and artificial intelligence technologies were necessary to prevent drowning accident, and qualification with limited authority should be added to the private safety guard because of the lack of regulation. Conclusion: In order to manage water safety effectively, a comprehensive water safety management system should be established that integrates people and equipment through systemic education of security personnel, authorization of enforcement, and introduction of surveillance equipment.

• Journal of IKEEE
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• v.26 no.2
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• pp.141-149
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• 2022

With the development of artificial intelligence and unmanned technologies, the remote surveillance/autonomous driving systems have been actively researched. For an effective performance analysis of the developed remote control system, it is important to record the data of it in real time. In addition, in order to analyze the performance between the control system and the remote system, the recorded data from them should be synchronized with time. In this paper we proposed a novel time synchronization method for the remote control system. The proposed remote control system satisfies the time difference of the recorded data within 1 ms, and we can reduce the time difference by using a CPU shielding and affinity setting. The performance of the proposed method was proved through various network data storage experiments. And the experiments confirmed that the proposed method can be applied to recording devices of unmanned ground vehicles and control vehicles. The proposed method will be used as a method for analyzing network data of UGV-R (Unmanned Ground Vehicle - Reconnaissance).