• International journal of advanced smart convergence
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• 제9권4호
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• pp.120-123
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• 2020

The surveillance system to prevent crime and accidents in advance has become a necessity, not an option in real life. Not only public institutions but also individuals are installing surveillance cameras to protect their property and privacy. However, since the installed surveillance camera cannot be monitored for 24 hours, the focus is on the technology that tracks the video after an accident occurs rather than prevention. In this paper, we propose a system model that monitors abnormal behaviors that may cause crimes through real-time video, and when a specific behavior occurs, the surveillance system automatically detects it and responds immediately through an alarm. We are a model that analyzes real-time images from surveillance cameras and uses I3D models from analysis servers to analyze abnormal behavior and deliver notifications to web servers and then to clients. If the system is implemented with the proposed model, immediate response can be expected when a crime occurs.

• International Journal of Internet, Broadcasting and Communication
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• 제16권4호
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• pp.168-173
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• 2024

This paper proposes a solution for innovating crime prevention and real-time response through the development of the Smart Drone Police System. The system integrates big data, artificial intelligence (AI), the Internet of Things (IoT), and autonomous drone driving technologies [2][5]. It stores and analyzes crime statistics from the Statistics Office and the Public Prosecutor's Office, as well as real-time data collected by drones, including location, video, and audio, in a cloud-based database [6][7]. By predicting high-risk areas and peak times for crimes, drones autonomously patrol these identified zones using a self-driving algorithm [5][8]. Equipped with video and voice recognition technologies, the drones detect dangerous situations in real-time and recognize threats using deep learning-based analysis, sending immediate alerts to the police control center [3][9]. When necessary, drones form an ad-hoc network to coordinate efforts in tracking suspects and blocking escape routes, providing crucial support for police dispatch and arrest operations [2][11]. To ensure sustained operation, solar and wireless charging technologies were introduced, enabling prolonged patrols that reduce operational costs while maintaining continuous surveillance and crime prevention [8][10]. Research confirms that the Smart Drone Police System is significantly more cost-effective than CCTV or patrol car-based systems, showing a 40% improvement in real-time response speed and a 25% increase in crime prevention effectiveness over traditional CCTV setups [1][2][14]. This system addresses police staffing shortages and contributes to building safer urban environments by enhancing response times and crime prevention capabilities [4].

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.336-338
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• 2015

This research is to propose an applicable framework for real-time health surveillance and safety monitoring at construction sites. First this study aims at finding (1) a framework for health surveillance that is likely to benefit employers and employees in the industry, (2) a valid way to identify factors or conditions with potential health concerns that can occur under particular work conditions, (3) An effective way to apply wireless/mobile sensors to construction workers using real-time/live data transmission methods, and (4) A relationship between a worker's vital signs and job site environment. Biosensors for physiological response and devices for weather/work related data are to collect real-time data. Relationships between jobs and physiological responses are analyzed and factors that touched particularly contributing to certain responses are identified. When data are incorporated with tasks, factors affecting tasks can be identified to estimate the magnitude of the factors. By comparing work and normal responses possible precautionary actions can be considered. In addition, the study would be lead to improving (1) trade-specific dynamic work schedules for workers which would be based on various factors affecting worker health level and (2) reevaluating worker productivity with health status and work schedule, thereby seeking ways to maximize worker productivity. Through a study, the paper presents expected benefits of implementing health monitoring.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• 한국인터넷방송통신학회논문지
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• 제19권2호
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• pp.161-168
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• 2019

최근에 빠르게 확산되고 있는 CCTV와 같은 영상기기들은 거의 모든 공공기관, 기업, 가정 등에서 비정상적인 상황을 감시하고 대처하기 위한 수단으로 활용되고 있다. 그러나 대부분의 경우 이상상황에 대한 인식은 모니터링하고 있는 사람에 의해 수동적으로 이루어지고 있어 즉각적인 대처가 미흡하며 사후 분석용으로만 활용되고 있다. 본 논문에서는 최신 딥러닝 기술과 실시간 전송기술을 활용하여 이벤트 발생시 스마트폰으로 이상 상황을 동영상과 함께 실시간으로 전송하는 동영상 감시 시스템의 개발 결과를 제시한다. 개발된 시스템은 오픈포즈 라이브러리를 이용하여 실시간으로 동영상으로 부터 인간 객체를 스켈레톤으로 모델링한 후, 딥러닝 기술을 이용하여 인간의 행동을 자동으로 인식하도록 구현하였다. 이를 위해 Caffe 프레임워크를 개발된 오픈포즈 라이브러리를 다크넷 기반으로 재구축하여 실시간 처리 능력을 대폭 향상 시켰으며, 실험을 통해 성능을 검증하였다. 본 논문에서 소개할 시스템은 정확하고 빠른 행동인식 성능과 확장성을 갖추고 있어 다양한 용도의 동영상 감시 시스템에 활용될 수 있을 것으로 기대된다.

• 한국융합학회논문지
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• 제8권3호
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• pp.15-22
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• 2017

최첨단 과학기술 시대를 맞아 사람들은 재난재해 예경보 시스템 및 재난재해 대응 시스템들이 잘 갖추어져 있다고 믿고 있으나 세월호 사건 등에서 알 수 있듯이 현실에서는 제대로 된 재난재해 예경보 및 대응 시스템이 갖추어져 있지 않은 상황이다. 기존의 재난재해 예경보 시스템의 경우 대부분 효율성이 낮은 센서 정보를 기반으로 하고 있으며, 영상 정보는 모니터링 요원에 의해 수동적으로 감시되고 있다. 또한 인식된 재난 재해에 대해서도 어떻게 대응하고 처리할 것인지에 대한 대응 시스템과의 연계가 미흡하다. 이에 따라 본 논문에서 CCTV 영상정보를 기반으로 특정 재난재해의 발생여부 및 정도를 최대한 빠르고 정확하게 인식하고 위기대응 매뉴얼에 근거하여 이를 모든 관련부처나 담당자들에게 자동으로 통보함으로써 효과적인 위기대응이 가능한 CCTV 기반 재난재해 인식 및 실시간 위기 대응 기술을 제안한다.

• Journal of Information Processing Systems
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• 제10권1호
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• pp.1-22
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• 2014

Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.

• 한국멀티미디어학회논문지
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• 제16권11호
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• pp.1261-1271
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• 2013

Zoom tracking involves the automatic adjustment of the focus motor in response to the zoom motor movements for the purpose of keeping an object of interest in focus, and is typically achieved by moving the zoom and focus motors in a zoom lens module so as to follow the so-called "trace curve", which shows the in-focus motor positions versus the zoom motor positions for a specific object distance. Thus, one can simply implement zoom tracking by following the most closest trace curve after all the trace curve data are stored in memory. However, this approach is often prohibitive in practical implementation because of its large memory requirement. Many other zoom tracking methods such as GZT, AZT and etc. have been proposed to avoid large memory requirement but with a deteriorated performance. In this paper, we propose a new zoom tracking method called 'Approximate Feedback Zoom Tracking method (AFZT)' on DM36x-based IP network camera, which does not need large memory by approximating nearby trace curves, but generates better zoom tracking accuracy than GZT or AZT by utilizing focus value as feedback information. Experiments through real implementation shows the proposed zoom tracking method improves the tracking performance and works in real-time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1909-1928
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• 2021

At present, the Colombian government shares information on threats or vulnerabilities in the area of cybersecurity and cyberdefense, from other government agencies or departments, on an ad-hoc basis but not in real time, with the surveillance entities of the Government of the Republic of Colombia such as the Joint Command of Cybernetic Operations (CCOCI) and the Cybernetic Emergencies Response Team of Colombia (ColCERT). This research presents the MS-CSIRT (Management System Computer Security Incident Response Teams) methodology, that is used to unify the guidelines of a CSIRT towards a joint communication command in cybersecurity for the surveillance of Information Technology (IT), Technological Operations (TO), Internet Connection Sharing (ICS) or Internet of Things (IoT) infrastructures. This methodology evaluates the level of maturity, by means of a roadmap, to establish a CSIRT as a reference framework for government entities and as a guide for the areas of information security, IT and TO to strengthen the growth of the industry 4.0. This allows the organizations to draw a line of cybersecurity policy with scope, objectives, controls, metrics, procedures and use cases for the correct coordination between ColCERT and CCOCI, as support entities in cybersecurity, and the different companies (ICS, IoT, gas and energy, mining, maritime, agro-industrial, among others) or government agencies that use this methodology.

• 한국전자파학회논문지
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• 제24권7호
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• pp.737-747
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• 2013

개발된 3차원 탐색 레이더는 저속으로 기동 중인 작은 RCS(Radar Cross Section)를 갖는 다수의 표적을 TWS(Track While Scan) 방식을 활용하여 실시간으로 탐지, 추적하여 표적의 거리, 속도, 각도 정보를 추출하는 펄스도플러(pulsed-doppler) 방식의 레이더이다. 본 논문에서는 고속으로 기동하는 표적을 탐지하기 위한 일반적인 레이더 신호처리 기법과 비교하여 영속도 근처의 저속 표적을 탐지하기 위한 속도 채널 처리 및 클러터맵 생성기법, 생성된 클러터맵을 활용하여 표적 정보를 획득하기 위한 신호처리 기법을 기술하며, 고속 다중 DSP가 탑재된 상용(COTS: Commercial Off-The-Shelf) 보드를 활용하여 개발된 3차원 탐색 레이더용 실시간 신호처리기의 HW/SW 구조 설계 및 탑재 알고리즘, 시험 결과 등을 제시한다.