• Fire Science and Engineering
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• v.24 no.1
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• pp.24-30
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• 2010

An integrated type fire alarm control panel which is for the purpose of the ubiquitous environment application is reported. For the ubiquitous environments, mainly three additional functions upon its inherent capabilities are implemented. That is, wireless technology with ZigBee capability is applied for the interface of ZigBee detectors. Camera images can be displayed in the fire alarm control panel in which images are transferred via ethernet. For the time synchronization of the distributed fire alarm control panel, GPS module is introduced and implemented in the system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.73-74
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• 2020

본 논문에서는 구현 장치에 연결된 다양한 인터페이스를 갖는 영상취득장치들 중에서 선택된 장치의 영상을 무선망을 통해 다수의 영상수신장치들에게 해당 영상을 전송하고, 원격으로 연결을 관리하는 영상송신시스템 디자인[1]의 하드웨어 구현 내용을 기술한다. 구현된 영상송신시스템 보드는 활용 요구 환경에 맞춰 영상취득을 위한 고정된 4개의 컴포지트 및 범용 USB 인터페이스, 무선 송수신 인터페이스, 전반적인 제어를 위한 CPU 모듈 등으로 구성된다. 원격의 영상수신장치들은 제안하는 구현된 송신시스템에 접속하여 개별채널을 확보하고 선택한 영상취득장치의 해당 영상을 직접 수신하고 해당 영상취득장치를 제어할 수 있다. 이를 위해서 연결된 다수의 외부 영상수신장치들과의 연결관리와 해당 영상취득장치의 제어 등과 같은 기능들을 제공하기 위한 하드웨어 보드를 구현하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.536-539
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• 2020

길이 좁고 차도와 인도의 구분이 없는 골목길의 특성상 사각지대가 많고 보행자의 동선을 예측하기 힘들어 교통사고가 많이 발생하고 있다. 따라서 본 논문에서는 AI 를 활용, 영상 내 사물을 추적하여 골목길에서의 사고를 예방하는 시스템을 제안한다. 해당 시스템은 Object - Detection & Tracking 을 사용하여 보행자 및 차량을 식별·추적하여 두 개 이상의 사물이 동시에 교차로에 접근 시 사고 예방 알람을 발생시킨다. 이 시스템을 전국에 설치되어 있는 CCTV 에 활용하면 추가적인 비용과 설치 시간에 제한받지 않고 전국적으로 응용할 수 있을 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.789-792
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• 2008

범죄 예방과 자원 보호를 위해 CCTV 카메라를 이용하는 방범 시스템의 필요성은 점차 커지고 있다. 아날로그 형식에서부터 디지털 형식으로 발전된 형태의 방범 시스템이 개발되고 사용 중이지만, 비용이 높고 효율성이 떨어지는 문제가 있다. 본 논문에서는 웹 서비스 기반의 서버에 적외선 카메라를 연결하고 사용자가 사전 인지 없이도 클라이언트에서 실시간으로 침입을 탐지하여 적절하게 대처할 수 있는 방범 시스템을 설계하고 구현한다.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.598-605
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• 2018

Behavior awareness is a technology that recognizes human behavior through data and can be used in applications such as risk behavior through video surveillance systems. Conventional behavior recognition algorithms have been performed using the 2D camera image device or multi-mode sensor or multi-view or 3D equipment. When two-dimensional data was used, the recognition rate was low in the behavior recognition of the three-dimensional space, and other methods were difficult due to the complicated equipment configuration and the expensive additional equipment. In this paper, we propose a method of recognizing human behavior using only CCTV images without additional equipment using only RGB and depth information. First, the skeleton extraction algorithm is applied to extract points of joints and body parts. We apply the equations to transform the vector including the displacement vector and the relational vector, and study the continuous vector data through the RNN model. As a result of applying the learned model to various data sets and confirming the accuracy of the behavior recognition, the performance similar to that of the existing algorithm using the 3D information can be verified only by the 2D information.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.143-148
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• 2016

There is increasing concern about security as a need for increased safety in the information industry society. However, it does not meet the needs for safety including CCTV. Therefore, in this paper, we link the processing technology using the image information to the IPS system consisting of GPS and Beacon. It designed a conventional RFID tag attached discomfort and image tracking system is limited to complement the disadvantages identifiable area. To this end, we designed a smart device and the Internet of Things convergence system and a research to ensure the accuracy and reliability of the IPS of the access control system. Finally, by leveraging intelligent video information using a PTZ camera, and set the entrant management policies it was carried out to control the situation and control. Also, by designing the integrated video tracking system, an authentication server, visualization systems were designed to establish an efficient technique for analyzing the IPS entrant behavior patterns.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.209-214
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• 2018

The subway, which is an urban railway, is the core of public transportation. Urban railways are always exposed to serious problems such as theft, crime and terrorism, as many passengers use them. Especially, due to the nature of urban railway environment, the scope of surveillance is widely dispersed and the range of surveillance target is rapidly increasing. Therefore, it is difficult to perform comprehensive management by passive surveillance like existing CCTV. In this paper, we propose the implementation, design method and object recognition algorithm for intelligent object recognition system in urban railway. The object recognition system that we propose is to analyze the camera images in the history and to recognize the situations where there are objects in the landing area and the waiting area that are not moving for more than a certain time. The proposed algorithm proved its effectiveness by showing detection rate of 100% for Selected area detection, 82% for detection in neglected object, and 94% for motionless object detection, compared with 84.62% object recognition rate using existing Kalman filter.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.64-76
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• 2008

To increase road safety at blackspots, it is needed to develop a new method that can process before accident occurrence. Accident situation could result from traffic conflict. Traffic conflict decision technique has an advantage that can acquire and analyze data in time and confined space that is less through investigation. Therefore, traffic conflict technique is highly expected to be used in many application of road safety. This study developed traffic conflict decision program that can analyze and process from signalized intersection image. Program consists of the following functional modules: an image input module that acquires images from the CCTV camera, a Save-to-Buffer module which stores the entered images by differentiating them into background images, current images, difference images, segmentation images, and a conflict detection module which displays the processed results. The program was developed using LabVIEW 8.5 (a graphic language) and the VISION module library.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1511-1518
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• 2007

As studies on ubiquitous computing are actively conducted, desire for various services, including image transmission storage, search and remote monitoring. has been expanding into mobile environment as well as to PCs. while CCTV (closed circuit TV) and un DVR (Digital video Recording) are used in places where security service such as intrusion detection system is required, these are high-end equipment. So it is not easy for ordinary users or household and small-sized companies to use them. Besides, they are difficult to be carried and camera solution for mobile device does not support high-quality function and provides low-definition of QVGA for picture quality. Therefore, in this study, design and implementation of embedded system of high-definition image transmission for ubiquitous mobile device which is not inferior to PC or DVR are described. To this end, usage of dedicated CPU for mobile device and design and implementation of MPEG-4 H/W CODEC also are examined. The implemented system showed excellent performance in mobile environment, in terms of speed, picture quality.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.6
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• pp.7-14
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• 2019

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.