• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.137-140
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• 2002

This paper presents a new variational framework for detecting and tracking moving objects in image sequence. Motion detection is performed using Level Set Model. The original frame is used to provide th moving object boundaries Then, the detection and the tracking problem are addressed in a common framework that employs a inward-outward curve evolution function. This function is minimized using a gradient decent method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.108-115
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• 2013

A developed skill of an intelligent CCTV is also advancing by using its Image Acquisition Device. In this field, area for technique can be divided into Foreground Subtraction which detects individuals and objects in a potential observing area and a tracing technology which figures out moving route of individuals and objects. In this thesis, an improved algorism for a settled engine development, which is stable to change in both noise and illumination for detecting moving objects is suggested. The proposed algorism from this thesis is focused on designing a stable and real time processing method which is perfect model in detecting individuals, animals, and also low-speeding transports and catching a change in an illumination and noise.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.289-299
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• 2018

The 360 degree panoramas are picture of a wide range of images on one screen, so we can see a fairly wide range at a time. In particular, cylinderical panoramas are the most widely used spherical image, and its left and right viewing angles reach 360 degree, so you can observe front, rear, left, and right at once. Using 360 degree panorama, all directions can be monitored at the same time, so all directions can be effectively monitored compared to other methods. In this paper, we develop a system to recognize and track the movement of moving objects on a 360 degree panorama, and then present and verify the experimental results. For this goals, first, we developed a system to recognize moving objects in 360 degree panorama using DoF(Difference of Frame) algorithm. Second, based on the TLD algorithm, we developed an application that can track a specific single moving object in a 360 degree panorama and presented the experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1671-1678
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• 2006

A real time system realization for binocular eyeball tracking mouse on the computer monitor being far from 30-40cm is presented in the paper. The processing for searching eyeball and tracking the cursor are that a facial image is acquired by the small CCD camera, convert it into binary image, search for the eye two using the five region mask method in the eye surroundings and the side four points diagonal positioning method is searched the each iris. The tracking cursor is matched by measuring the iris central moving position. The cursor controlling is achieved by comparing two related distances between the iris maximum moving and the cursor moving to calculate the moving distance from gazing position and screen. The experimental results show that the binocular eyeball mouse system is simple and fast to be real time.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.119-124
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• 2009

In this paper, we propose a novel approach to estimating the real-time moving trajectory of an object is proposed in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the input-output relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• Journal of IKEEE
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• v.19 no.1
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• pp.58-63
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• 2015

This study suggested the mobile intelligent management system based on the cloud service. The mobile intelligent management system are composed of cloud server, middleware and sensor networks. Each modules are controlled on mobile environment and observed operating status of each apparatus for environment. In this pater, the image-based moving detection algorithm applied in order to detect an intruder and average 12.3% are measured in moving detection experiments. it was confirmed the validity of the security device.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.11
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• pp.1138-1144
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• 2009

The velocity and acceleration of the ground moving target can cause the target position to be displaced and defocused in the SAR image. In this paper, the displacement compensation scheme is presented to correct the displaced position and defocused moving target image in the DPCA based SAR-GMTI system. The influence of the ground moving target due to the velocity and acceleration is analyzed in range and azimuth directions, and its compensation method is presented with the simulation results. The performance of the proposed method is compared with respect to the estimated velocity and defocused quantity in both range and azimuth directions.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.93-103
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• 1999

To detect obstacles and lane-markers for driving vehicles, a new moving window scheme where moving windows are assigned to an image frame captured by a camera is addressed. For the detection of obstacles, it is important to estimate lane-markers precisely and rapidly. For this purpose, selecting some partes of an image frame at the expected lane locations, i.e., selecting window are generally adopted for extracting lane-markers efficiently. In this paper, a new scheme that extracts lane-markers precisely by assigning variable size windows at the expected locations of lane-markers considering the road curvature and finally detects obstacles within a driving lane is proposed. The accuracy improvement using this moving window scheme is showed by comparing to the conventional fixed window method and to using radar to laser sensors.

• Journal of Information Display
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• v.6 no.4
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• pp.16-25
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• 2005

In this paper, a new 3D object tracking system using the disparity motion vector (DMV) is presented. In the proposed method, the time-sequential disparity maps are extracted from the sequence of the stereo input image pairs and these disparity maps are used to sequentially estimate the DMV defined as a disparity difference between two consecutive disparity maps Similarly to motion vectors in the conventional video signals, the DMV provides us with motion information of a moving target by showing a relatively large change in the disparity values in the target areas. Accordingly, this DMV helps detect the target area and its location coordinates. Based on these location data of a moving target, the pan/tilt embedded in the stereo camera system can be controlled and consequently achieve real-time stereo tracking of a moving target. From the results of experiments with 9 frames of the stereo image pairs having 256x256 pixels, it is shown that the proposed DMV-based stereo object tracking system can track the moving target with a relatively low error ratio of about 3.05 % on average.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.157-160
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• 2011

In this paper, We proposed the method of recognition and analysis of human activites using Motion vector in real-time surveillance system. We employs subtraction image techniques to detect blob(human) in the foreground. When MPEG-4 video recording EPZS(Enhanced Predicted Zonal Search) is detected the values of motion vectors were used. In this paper, the activities of human recognize and classified such as meta-classes like this {Active, Inactive}, {Moving, Non-moving}, {Walking, Running}. Each step was separated using a step-by-step threshold values. We created approximately 150 conditions for the simulation. As a result, We showed a high success rate about 86~98% to distinguish each steps in simulation image.