• Journal of Electrical Engineering and Technology
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• v.6 no.3
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• pp.391-396
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• 2011

The tracking pattern formed on the dielectric surface due to a surface electrical discharge exhibits fractal structure. In order to quantitatively investigate the fractal characteristics of the surface tracking pattern, the dielectric breakdown model has been employed to numerically generate the surface tracking pattern. In dielectric breakdown model, the pattern growth is determined stochastically by a probability function depending on the local electric potential difference. For the computation of the electric potential for all points of the lattice, a two-dimensional discrete Laplace equation is solved by mean of the successive over-relaxation method combined to the Gauss-Seidel method. The box counting method has been used to calculate the fractal dimensions of the simulated patterns with various exponent $\eta$ and breakdown voltage $\phi_b$. As a result of the simulation, it is found that the fractal nature of the surface tracking pattern depends strongly on $\eta$ and $\phi_b$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.779-787
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• 2010

In this paper, We introduce electrical tracking generated from surface activity associated with flow of leakage current on insulator under wet and contaminated conditions and design electrical tracking pattern recognition system by using LabVIEW. We measure the leaking current of contaminated wire by using LabVIEW software and the NI-c-DAQ 9172 and NI-9239 hardware. As pattern recognition algorithm and optimization algorithm for electrical tracking system, neural networks, Radial Basis Function Neural Networks(RBFNNs) and particle swarm optimization are exploited. The designed electrical tracking recognition system consists of two parts such as the hardware part of electrical tracking generator, the NI-c-DAQ 9172 and NI-9239 hardware and the software part of LabVIEW block diagram, LabVIEW front panel and pattern recognition-related application software. The electrical tracking system decides whether electrical tracking generate or not on electrical wire.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.523-526
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• 1996

In this paper, robot endeffector tracking using sensory information from structured laser pattern diode, is described. In order to track robot endeffector robustly irrespective of translation, scaling and rotation of robot working tool, structured laser pattern is used as track feature. Structured laser patterns of crosshair, concentric circles, dot matrix, and parallel lines are illuminated to robot endeffector. Illuminated laser patterns are held invariently and coherently irrespective of various motions of robot endeffector. Extracting and tracking these invariant structured laser patterns as track feature, the whole system keeps tracking of the robot endeffector robustly and effectively provided that structured laser pattern is always assumed to aim at robot endeffector.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.47-54
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• 2017

Recently, correlation filter based trackers have shown excellent tracking performance and computational efficiency. In order to enhance tracking performance in the correlation filter based tracker, search area which is image patch for finding target must include target. In this paper, two methods to discriminatively represent target in the search area are proposed. Firstly, search area location is estimated using pyramidal Lucas-Kanade algorithm. By estimating search area location before filtering, fast motion target can be included in the search area. Secondly, we investigate multi-channel Local Edge Pattern(LEP) which is insensitive to illumination and noise variation. Qualitative and quantitative experiments are performed with eight dataset, which includes ground truth. In comparison with method without search area estimation, our approach retain tracking for the fast motion target. Additionally, the proposed multi-channel LEP improves discriminative performance compare to existing features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.305-326
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• 2019

Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates.

• Journal of Korea Game Society
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• v.20 no.6
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• pp.43-52
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• 2020

This paper presents a fast pattern tracking method using location prediction in cubemap video for 360-degree VR. A spherical cubemap frame has six face textures and searching a pattern is much slower than a flat image. To overcome the limitation, we propose a method of predicting the location of target pattern using Kalman filter and reducing the search area by considering only textures of predicted location. The experimental results showed that the proposed system is much faster than the previous method of searching all six faces and also gives accurate pattern tracking performance.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.645-654
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• 2019

The purpose of this study was to collect and analyze personal bio data and social network services (SNS) data, derive user preference and user life pattern, and propose intuitive and precise user modeling. This study not only tried to conduct eye tracking experiments using various smart devices to be the ground of the recommendation system considering the attribute of smart devices, but also derived classification preference by analyzing eye tracking data of collected bio data and SNS data. In addition, this study intended to combine and analyze preference of the common classification of the two types of data, derive final preference by each smart device, and based on user life pattern extracted from final preference and collected bio data (amount of activity, sleep), draw the similarity between users using Pearson correlation coefficient. Through derivation of preference considering the attribute of smart devices, it could be found that users would be influenced by smart devices. With user modeling using user behavior pattern, eye tracking, and user preference, this study tried to contribute to the research on the recommendation system that should precisely reflect user tendency.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.818-820
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• 1999

In this paper, a new boundary tracking algorithm for extracting finger area, which may be utilized by a finger crease pattern recognition algorithm, is proposed. Due to noise and irregular illumination, conventional algorithms for boundary tracking such as skeleton-based tracking methods were not suitable for typical boundary image of hand. So we propose a new finger boundary tracking algorithm utilizing a boundary-point-detection mask. We have observed that the proposed method provides stable and optimised boundary tracking.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.903-909
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• 2018

We propose laser scanner sensor system based walking pattern and tracking method of multiple human. This system uses laser scanners sensors and is applicable to wide and crowded area such as hospital and medical care center. The primary objective of this research is to promote the development of robust, repeatable and transferable software for security system that can automatically detect, track and follow people in public area. We developed the method of human identification for this system. Our method is following: 1. Best-walking pattern data are obtained by the help of human position and direction data obtained by laser scanners. 2. Human identification is conducted by calculating the correlation between the step length of walking human. It becomes possible to conduct human identification even in crowded scenes by estimating the movements of waling human' feet are periodic. In the experiment in the station, some effectiveness of this method became clear.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.