• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.531-534
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• 2006

능동 카메라에서 배경과 물체가 모두 움직이는 영상에서 이동물체를 검출하여 추적하기 위해 특징점을 추출하고 특징점을 이용해 영상 좌표계 변환 파라미터를 추정하여 카메라의 Ego-motion을 보정한다. 보정된 영상을 이용하여 움직이는 물체를 검출하고 잡음이 있는 관측영역에서 CONDENSATION 알고리즘을 이용하여 이동물체를 추정하는 실험을 수행한 내용의 논문이다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.63-77
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• 1994

This paper describes the real-time system which, through analyzing a sequence of images, can extract motional information on a moving object and can contol servo equipment to always locate the moving object at the center of an image frame. An image is a vast amount of two-dimensional signal, so it takes a lot of time to analyze the whole quantity of a given image. Especially, the time needed to load pixels from a memory to processor increase exponentially as the size of an image increases. To solve such a problem and track a moving object in real-time, this paper addresses how to selectively search the spatial and time domain. Based on the selective search of spatial and time domain, this paper suggests various types of techniques which are essential in implementing a real-time tracking system. That is, this paper describes how to detect an entrance of a moving object in the field of view of a camera and the direction of the entrance, how to determine the time interval of adjacent images, how to determine nonstationary areas formed by a moving object and calculated velocity and position information of a moving object based on the determined areas, how to control servo equipment to locate the moving object at the center of an image frame, and how to properly adjust time interval(${\Delta}$t) to track an object taking variable speed.

• Journal of Advanced Navigation Technology
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• v.9 no.1
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• pp.48-55
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• 2005

In this paper, we propose an efficient moving object tracking technique for multi-camera surveillance system. Color CCD cameras used in this system are network cameras with their own IP addresses. Input image is transmitted to the media server through wireless connection among server, bridge, and Access Point (AP). The tracking system sends the received images through the network to the tracking module, and it tracks moving objects in real-time using color matching method. We compose two sets of cameras, and when the object is out of field of view (FOV), we accomplish hand-over to be able to continue tracking the object. When hand-over is performed, we use MHI(Motion History Information) based on color information and M-bin histogram for an exact tracking. By utilizing MHI, we can calculate direction and velocity of the object, and those information helps to predict next location of the object. Therefore, we obtain a better result in speed and stability than using template matching based on only M-bin histogram, and we verified this result by an experiment.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.207-209
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• 2009

다양한 센서 및 영상 카메라를 이용한 교통, 보안 및 안전 감시 시스템에 있어 처리해야 하는 영상 데이터의 양은 점점 커져가고 있다. 또한 단일 카메라가 아닌 많은 수의 카메라를 이용할 경우 운영자가 모든 영상 데이터를 확인하고 이에 대한 응답을 즉시 하기가 힘이 든다. 따라서 영상 데이터를 이용하기 위한 시스템에서 소프트웨어적인 처리는 필수이며 물체를 정확하게 추적하기 위해서는 물체를 인식하고 물체의 움직임을 예측하고 움직임을 보정하는 단계가 필요하다. 본 논문에서는 물체의 움직임을 정확히 추적하기 위하여 이동 물체를 추적할 때에 적절한 Kalman 필터를 이용하여 고속 물체 추적 시스템을 구현하였다.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.684-690
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• 2010

Digitalized Image Information is variously used like to substitute or help human's visual ability. Unmanned observation Camera is useful for the preventing disaster, risk factor and object observation but it is mostly to depend on awareness for human's vision. The purpose of this paper is to show that Unmanned Aerial Camera carries out object recognition and autonomous position tracking. when the informations about a specific object are given. For this purpose, we have to solve complicated problems like change according to object movement and variation of color and brightness information with refraction, interference and scattering of light and noise from environmental factors like weather. But, as the first step we limit the scope of this study with simplified environment in this paper. Our goal is the study and experience about object recognition and tracking via simplified environment with unmanned aerial camera. We obtained successful results of this study and experiment.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.54-62
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• 2006

In this paper, a new stereo object tracking system using the disparity motion vector is proposed. In the proposed method, the time-sequential disparity motion vector can be estimated from the disparity vectors which are extracted from the sequence of the stereo input image pair and then using these disparity motion vectors, the area where the target object is located and its location coordinate are detected from the input stereo image. Basing on this location data of the target object, the pan/tilt embedded in the stereo camera system can be controlled and as a result, stereo tracking of the target object can be possible. From some experiments with the 2 frames of the stereo image pairs having $256\times256$ pixels, it is shown that the proposed stereo tracking system can adaptively track the target object with a low error ratio of about 3.05 % on average between the detected and actual location coordinates of the target object.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.166-174
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• 2006

In this paper, a new stereo object tracking system using the disparity motion vector is proposed. In the proposed method, the time-sequential disparity motion vector can be estimated from the disparity vectors which are extracted from the sequence of the stereo input image pair and then using these disparity motion vectors, the area where the target object is located and its location coordinate are detected from the input stereo image. Being based on this location data of the target object, the pan/tilt embedded in the stereo camera system can be controlled and as a result, stereo tracking of the target object can be possible. From some experiments with the 2 frames of the stereo image pairs having 256$\times$256 pixels, it is shown that the proposed stereo tracking system can adaptively track the target object with a low error ratio of about 3.05$\%$ on average between the detected and actual location coordinates of the target object.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.402-409
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• 2007

In this paper recognized for trajectory using Condensation algorithm. In this pater used fuzzy controller for recognized trajectory using fuzzy reasoning. The fuzzy system tract to the three-dimensional space for raw and roll movement. The joint angle ${\theta}_1$ of the manipulator rotate from $0^{\circ}\;to\;360^{\circ}$, and the joint angle ${\theta}_2$ rotate from $0^{\circ}\;to\;180^{\circ}$. The moving object of velocity display for recognition without error using Condensation algorithm. The tracking system demonstrated the reliability of proposed algorithm through simulation against used trajectory.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.35-41
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• 2015

When a video surveillance system tracks a specific object, it is very important to get quickly the information of the object through fast image processing. Usually one camera surveillance system for tracking the object made results in various problems such like occlusion, image noise during the tracking process. It makes difficulties on image based moving object tracking. Therefore, to overcome the difficulties the multi video surveillance system which installed several camera within interested area and looking the same object from multi angles of view could be considered as a solution. If multi cameras are used for tracking object, it is capable of making a decision having high accuracy in more wide space. This paper proposes a method of recognizing and tracking a specific object like a car using the homography in which multi cameras are installed at the crossroad.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.153-163
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• 2004

This paper presents a video tracking method for a deformable moving object using an active contour model in the image sequences. It is quite important to decide the local convergence directions of the contour points for correctly extracting the boundary of the moving object with deformable shape. For this purpose, an energy function for the active contour model is newly proposed by adding a directional energy term using a frame difference map to tile Greedy algorithm. In addition, an updating rule of tile frame difference map is developed to encourage the stable convergence of the contour points. Experimental results on a set of synthetic and real image sequences showed that the proposed method can fully track the deformable object while extracting the boundary of the object elaborately in every frame.