• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 춘계학술대회 논문집
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• pp.1722-1728
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• 2011

The security issue in urban transit system has been widely considered as the common matters after the fire accident at Daegu subway station. The safe urban transit system is highly demanded because of the vast number of daily passengers, and it is one of the most challenging projects. We introduced a test model for integrated security system for urban transit system and built it at a subway station to demonstrate its performance. This system consists of cameras, sensor network and central monitoring software. We described the smart camera functionality in more detail. The proposed smart camera includes the moving objects recognition module, video analytics, video encoder and server module that transmits video and audio information.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.912-928
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• 2019

Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast. Thus, it is significant in the future video surveillance systems. Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior. Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations. Most existing studies on group detection are scene-specific. Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes. Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories. This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. It also tackles grouping consistency between frames. Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results.

• 한국컴퓨터정보학회논문지
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• 제22권7호
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• pp.101-108
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• 2017

A wireless image sensor node collecting image data for environmental monitoring or surveillance requires a large amount of energy to transmit the huge amount of video data. Even though solar energy can be used to overcome the energy constraint, since the collected energy is also limited, an efficient energy management scheme for transmitting a large amount of video data is needed. In this paper, we propose a method to reduce the number of blackout nodes and increase the amount of gathered data by selecting an appropriate video coding method according to the energy condition of the node in a solar-powered wireless video sensor network. This scheme allocates the amount of energy that can be used over time in order to seamlessly collect data regardless of night or day, and selects a high compression coding method when the allocated energy is large and a low compression coding when the quota is low. Thereby, it reduces the blackout of the relay node and increases the amount of data obtained at the sink node by allowing the data to be transmitted continuously. Also, if the energy is lower than operating normaly, the frame rate is adjusted to prevent the energy exhaustion of nodes. Simulation results show that the proposed scheme suppresses the energy exhaustion of the relay node and collects more data than other schemes.

• 한국군사과학기술학회지
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• 제15권6호
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• pp.761-769
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• 2012

In this paper, we propose a low-power design strategy to minimize energy-consumption for surveillance and reconnaissance sensor networks. The sensor network consists of many different nodes with various operations such as target detection, packet relay, video monitoring, changing protocols, and etc. Each sensor node consists of sensing, computing, communication, and power components. These components are integrated on a single or multiple boards. Therefore, the power consumption of each component can be different on various operation types. First, we identified the list of components and measured power consumption for them from the first prototype nodes. Next, we focus on which components are the main sources of energy consumption. We propose many energy-efficient approaches to reduce energy consumption for each operation type.

• 대한원격탐사학회지
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• 제24권3호
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• pp.235-243
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• 2008

Object tracking is a common vision task to detect and trace objects between consecutive frames. It is also important for a variety of applications such as surveillance, video based traffic monitoring system, and so on. An efficient moving vehicle clustering and tracking algorithm suitable for traffic monitoring system is proposed in this paper. First, automatic background extraction method is used to get a reliable background as a reference. The moving blob(object) is then separated from the background by mean shift method. Second, the scale invariant feature based method extracts the salient features from the clustered foreground blob. It is robust to change the illumination, scale, and affine shape. The simulation results on various road situations demonstrate good performance achieved by proposed method.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.216-224
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• 2017

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.

• 조명전기설비학회논문지
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• 제28권9호
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• pp.99-104
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• 2014

This paper presents a unified live-streaming method based on Hypertext Mark-up Language 5(HTML5) for an IP camera which is independent of browsers of clients and is implemented with open-source libraries. Currently, conventional security systems based on analog CCTV cameras are being modified to newer surveillance systems utilizing IP cameras. These cameras offer remote surveillance and monitoring regardless of the device being used at any time, from any location. However, this approach needs live-streaming protocols to be implemented in order to verify real-time video streams and surveillance is possible after installation of separate plug-ins or special software. Recently, live streaming is being conducted through HTML5 using two different standard protocols: HLS and DASH, that works with Apple and Android products respectively. This paper proposes a live-streaming approach that is linked on either of the two protocols which makes the system independent with the browser or OS. The client is possible to monitor real-time video streams without the need of any additional plug-ins. Moreover, by implementing open source libraries, development costs and time were economized. We verified usefulness of the proposed approach through mobile devices and extendability to other various applications of the system.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• 센서학회지
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• 제29권3호
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• pp.194-200
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• 2020

Human detection is an important aspect in many video-based sensing and monitoring systems. Studies have been actively conducted for the automatic detection of humans in camera images, and various methods have been proposed. However, there are still problems in terms of performance and computational cost. In this paper, we describe a method for efficient human detection in the field of view of a camera, which may be static or moving, through multiple processing steps. A detection line is designated at the position where a human appears first in a sensing area, and only the one-dimensional gray pixel values of the line are monitored. If any noticeable change occurs in the detection line, corner detection and optical flow computation are performed in the vicinity of the detection line to confirm the change. When significant changes are observed in the corner numbers and optical flow vectors, the final determination of human presence in the monitoring area is performed using the Histograms of Oriented Gradients method and a Support Vector Machine. The proposed method requires processing only specific small areas of two consecutive gray images. Furthermore, this method enables operation not only in a static condition with a fixed camera, but also in a dynamic condition such as an operation using a camera attached to a moving vehicle.