• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.1040-1047
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• 2012

In this paper, we propose a ball tracking algorithm robust against occlusion in broadcasting soccer video sequences. Soccer ball tracking is a challenging task due to occlusion, fast motion and fast direction changes. Many works have been proposed based on ball trajectory. However, this approach requires heavy computational complexity. We propose a ball tracking algorithm with occlusion handling capability. Initial ball location is calculated using the circular hough transform. Then, the ball is tracked using template matching. Occlusion is handled by matching score. In occlusion cases, we generate a set of ball candidates. The ball candidates which exist in the previous frame were removed. On the other hand, the new appearing candidate is determined as the ball. Experiments with several broadcasting soccer video sequences show that the proposed method efficiently handles the occlusion cases.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.463-463
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• 2000

A dual-stage positioner for fast and fine robotic manipulations is presented. By adopting the merits of both coarse and fine actuator, a desirable system having the capacity of large workspace with high resolution of motion is enabled. We have constructed an ultra precision XY positioner with dual-stage mechanism where the PZT driven fine stage is mounted on the motor driven XY positioner and applied it to fine tracking controls and micro-tele operations as a slave manipulator. We describe essential merits of the compound actuation mechanism and some control strategies to successfully utilize it with proper servo system design. Through experimental results, the effectiveness of the coarse/fine manipulation by the dual-stage positioner will be shown.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.283-286
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• 2002

This paper presents the straightness compensation system which is a device for improving the machining accuracy of ultra-precision machines by synchronizing the position of diamond tool tip with machine error motion. Sine it is actuated by piezoelectric actuator with highly nonlinear hysteresis characteristics, the feedback control schemes such as Proportional Integral(PI), are required and realized by measuring the displacements of diamond tool tip. for the better tracking performance, the controller was implemented using TMS320C32 32bit floating-point DSP which is fast so that the real-time control is possible. In addition, stand alone type DSP board was chosen fur the easy assembly into the ultra-precision machines. The experimental results show good command tracking performance and the motion error of the machine is satisfactorily compensated during the machining process.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.395-398
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• 2006

In this paper, for implementing a real-time software GNSS receiver we propose the highly efficient and fast algorithms such as partial down-conversion, phase rotator, composite I&Q accumulation, Virtual DCO technique, and parallel acquisition using FFT. When the proposed algorithms are used, more 30 tracking channels with 3 tracking arm(early-prompt-late) is operated real-time on Intel 2.8GHz personal computer. Also, the partial down-conversion reduces the FFT size, for parallel acquisition, to 1/8 of conventional FFT-size and the program size includes map is not exceed 1Mbyte. Finally, the proposed real-time software GNSS receiver using the proposed algorithms provides the navigation solution with below 10 meter rms error.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.740-745
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• 2003

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks suck as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed no the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake will not operate well because the moving object may have large differences in its position or shape, between successive images. Snakes's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model(SCSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.707-720
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• 2014

This paper proposes a face tracking scheme that is a combination of a face detection algorithm and a face tracking algorithm. The proposed face detection algorithm basically uses the Adaboost algorithm, but the amount of search area is dramatically reduced, by using skin color and motion information in the depth map. Also, we propose a face tracking algorithm that uses a template matching method with depth information only. It also includes an early termination scheme, by a spiral search for template matching, which reduces the operation time with small loss in accuracy. It also incorporates an additional simple refinement process to make the loss in accuracy smaller. When the face tracking scheme fails to track the face, it automatically goes back to the face detection scheme, to find a new face to track. The two schemes are experimented with some home-made test sequences, and some in public. The experimental results are compared to show that they outperform the existing methods in accuracy and speed. Also we show some trade-offs between the tracking accuracy and the execution time for broader application.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.167-177
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• 2019

Even though so much progresses have been achieved in Multiple Object Tracking (MOT), most of reported MOT methods are not still satisfactory for commercial embedded products like Pan-Tilt-Zoom (PTZ) camera. In this paper, we propose a real-time multiple pedestrians tracking method for embedded environments. First, we design a new light weight convolutional neural network(CNN)-based pedestrian detector, which is constructed to detect even small size pedestrians, as well. For further saving of processing time, the designed detector is applied for every other frame, and Kalman filter is employed to predict pedestrians' positions in frames where the designed CNN-based detector is not applied. The pose orientation information is incorporated to enhance object association for tracking pedestrians without further computational cost. Through experiments on Nvidia's embedded computing board, Jetson TX2, it is verified that the designed pedestrian detector detects even small size pedestrians fast and well, compared to many state-of-the-art detectors, and that the proposed tracking method can track pedestrians in real-time and show accuracy performance comparably to performances of many state-of-the-art tracking methods, which do not target for operation in embedded systems.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1102-1105
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• 2004

In this paper, the problem of a viewer's head tracking in a desktop-based interactive stereoscopic display system is considered. A fast and low-cost approach to the problem is important for such a computing environment. The system under consideration utilizes a shuttle glass for stereoscopic display. The proposed method makes use of an image taken from a single low-cost video camera. By using a simple feature extraction algorithm, the obtained points corresponding to the image of the user-worn shuttle glass are used to estimate the glass center, its local 'yaw' angle, as measured with respect to the glass center, and its global 'yaw' angle as measured with respect to the camera location. With these estimations, the stereoscopic image synthetic program utilizes those values to interactively adjust the two-view stereoscopic image pair as displayed on a computer screen. The adjustment is carried out such that the so-obtained stereoscopic picture, when viewed from a current user position, provides a close-to-real perspective and depth perception. However, because the algorithm and device used are designed for fast computation, the estimation is typically not precise enough to provide a flicker-free interactive viewing. An error concealment method is thus proposed to alleviate the problem. This concealment method should be sufficient for applications that do not require a high degree of visual realism and interaction.

• Journal of IKEEE
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• v.20 no.1
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• pp.103-106
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• 2016

PWM buck converters usually use a type-III error amplifier. Since this amplifier has a big capacitor with slow slew rate, they can generate an unintended large overshoot/undershoot at the output when a large load current change occurs. They can also respond slowly by varying the reference voltage. In order to increase battery lifetime, power supplies require a various range of load current and output voltage. PWM buck converter also should have a characteristic of both fast load response and reference tracking. This paper surveys a few recent techniques for reducing the settling time, and discusses their merits and limitations.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.524-527
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• 1997

Conventional target tracking algorithms based on the linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias in the measurement sequence. Accurate compensation for the bias requires processing more samples of which adds to the computational complexity. The primary motivation for employing a neural network for this task comes from the efficiency with which more features can be as inputs for bias compensation. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.