• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.23-29
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• 2010

Nowadays it is required to design intelligent visual surveillance systems which automatically detect abandoned objects in public places to strengthen the social safety. Already recognized abandoned objects can be occluded partially or fully by surrounding people in public places after the first recognition. To improve an essential recognition performance index PAT, the system should overcome the occlusion problems. In this research, a design scheme is newly proposed to construct the robust detection system which is comprised of multiple stages considering the occlusion problem. To show the feasibilities of the proposed system, the evaluation was tried for the prepared image streams including 6 various situations and the experimental results show 96% and 75% in PAT performance for intrusion and abandoning events, respectively. Finally in spite of full occlusions by multiple persons, the proposed system shows the capability to continuously recognize the abandoned object after complex occlusions disappear.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12C
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• pp.744-752
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• 2011

In intelligent surveillance system, various methods for detecting abnormal behavior were proposed recently. However, most researches are not robust enough to be utilized for actual reality which often has occlusions because of assumption the researches have that individual objects can be tracked. This paper presents a novel method to detect abnormal behavior by analysing major motion of the scene for complex environment in which object tracking cannot work. First, we generate Visual Word and Visual Document from motion information extracted from input video and process them through LDA(Latent Dirichlet Allocation) algorithm which is one of document analysis technique to obtain major motion information(location, magnitude, direction, distribution) of the scene. Using acquired information, we compare similarity between motion appeared in input video and analysed major motion in order to detect motions which does not match to major motions as abnormal behavior.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.5
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• pp.435-446
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• 2014

Even if it is important to transmit high rate multimedia information without any transmission errors for surveillance systems, it is difficult to achieve error-free transmission due to infra-less adhoc networks. In order to reduce the transmission errors furthermore, additional signal overheads or retransmission of signals should be required, but they may lead to transmission delay. This paper represents a study on low delay and adaptive video transmission for the unmanned surveillance systems by developing system protocols. In addition, we introduce an efficient and adaptive control algorithm using system parameters for exploiting unmanned surveillance system properly over multi-channels.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.13-19
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• 2011

In this paper, a visual surveillance system using SOPC based NIOS II embedded processor and C2H compiler was implemented. In this system, the IP is constructed by C2H compiler for the output of the camera images, image processing, serial communication and network communication, then, it is implemented to effectively control each IP based on the SOPC and the NIOS II embedded processor. And, an algorithm which updates the background images for high speed and robust detection of the moving objects is proposed using the Adaptive Gaussian Mixture Model(AGMM). In results, it can detecte the moving objects(pedestrians and vehicles) under day-time and night-time. It is confirmed that the proposed AGMM algorithm has better performance than the Adaptive Threshold Method(ATM) and the Gaussian Mixture Model(GMM) from our experiments.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2003.11a
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• pp.406-410
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• 2003

DUPIC Fuel Development Facility(DFDF) is the facility to fabricate CANDU-type fuel from spent PWR fuel material without any separation of fissile elements and fission products. Unattended continuous surveillance systems for safeguards of nuclear facility result in large amounts of image and radiation data, which require much time and effort to inspect. Therefore, it is necessary to develop system that automatically pinpoints and diagnoses the anomalies from data. In this regards, this paper presents a novel concept of the continuous surveillance system that integrates visual image and radiation data by the use of neural networks. This surveillance system is operating for safeguards of the DFDF in KAERI.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1199-1207
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• 2019

Video surveillance data, which is used for preemptive or post-emptive action against any event or accident, is required for monitoring the location, but is reducing the capacity of the image data by removing intervals for cost reduction and system persistence. Such a video surveillance system is fixed in a certain position and monitors the area only within a limited angle, or monitors only the fixed area without changing the angle. At this time, the video surveillance system that is monitored only within a limited angle shows that the variation object such as the floating population shows different status in the image, and the background of the image maintains a generally constant appearance. The static objects in the image do not need to be stored in all the images, unlike the dynamic objects that must be continuously shot, and occupy a storage space other than the necessary ones. In this paper, we propose a mechanism to analyze the image, store only the small size image for the fixed background, and store it as image data only for variable objects.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.159-161
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• 2004

Many motion object detection algorithms rely on the process of background subtraction, an important technique that is used for detecting changes from a model of the background scene. This paper propose a novel method to update the background model image of a visual surveillance system which is not stationary. In order to do this, we use a background model based on statistical qualities of monitored images and another background model that excluded motions. By comparing each changed area computed from the two background model images and current monitored image, the areas that will be updated are decided.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.264-266
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• 2006

This traveling wave tube amplifier maintenance system was developed for performance of microwave Surveillance radar traveling wave tube amplification parts, and field operation. The Maintenance system is traveling wave tube amplification part RF output waveform measurement and HVPS Voltage adjustment and a maintenance function are offer. The system was developed as an embedded system base it consisted of Linux os which applied a top-down design and visual technique. Therefore change and easy extension of a system. This paper discussed characteristic of maintenance equipment function, composition, and an employment program.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1917-1920
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• 2003

Automatic tracking of a moving object such as a person is a demanding technique especially in surveillance. This paper describes an experimental system for tracking a moving object on the ground by using a visually controlled aerial robot. A blimp is used as the aerial robot in the proposed system because of its locality in motion and its silent nature. The developed blimp is equipped with a camera for taking downward images and four rotors for controlling the progression. Once a camera takes an image of a specified moving object on the ground, the blimp is controlled so that it follows the object by the employment of the visual information. Experimental results show satisfactory performance of the system. Advantages of the present system include that images from the air often enable us to avoid occlusion among objects on the ground and that blimp’s progression is much less restricted in the air than, e.g., a mobile robot running on the ground.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.1-8
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• 2009

Detecting entry and exit zones in a view covered by multiple cameras is an essential step to determine the topology of the camera setup, which is critical for achieving and sustaining the accuracy and efficiency of multi-camera surveillance system. In this paper, a graph theoretic clustering method is proposed to detect zones using data points which correspond to entry and exit events of objects in the camera view. The minimum spanning tree (MST) is constructed by associating the data points. Then a set of well-formed clusters is sought by removing inconsistent edges of the MST, based on the concepts of the cluster balance and the cluster density defined in the paper. Experimental results suggest that the proposed method is effective, even for sparsely elongated clusters which could be problematic for expectation-maximization (EM). In addition, comparing to the EM-based approaches, the number of data required to obtain stable outcome is relatively small, hence shorter learning period.