• ETRI Journal
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• v.41 no.4
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• pp.483-493
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• 2019

Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.

• Proceedings of the KIEE Conference
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• 2004.10a
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• pp.221-223
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• 2004

Accurate and reliable tracking system is essential for the central train control system. There are two systems widely being used, one is the fixed block system which utilizes the track circuit for the detection of train position, the other is the moving block system which has the advantage over the former since it enables shorter radio signal for the train position detection. In this paper a new algorithm is proposed which uses signal's phase difference of arrival to detect the train position. Experimental verification of the algorithm is presented in the paper.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.275-281
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• 2005

In this paper, a new algorithm is proposed for high speed roaming in an Intelligent Train Control System(ITCS) and study on optimized positioning of radio communication equipment. A DCS(Data Communication System) which is a main part of CBTC(Communication Based Train Control) system, is consisted of radio based wireless communication system and wired optical system. In the radio based wireless communication, the position of AP enclosures and antennas shall be optimized for the guaranteed communication channel between wayside and trains both in open aired and tunnelled area. Also a communication channel established between wayside and train shall be maintained while train moves at its maximum speed. This study shows the way of determining the optimal position for the railway side communication equipment in Bundang Line and how to achieve continuous communicating channel for tracking and controlling train.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1229-1234
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• 2004

Accurate and reliable tracking system is essential for the central train control system. There are two systems widely being used. one is the fixed block system which utilizes the track circuit for the detection of train position. the other is the moving block system which has the advantage over the former since it enables shorter radio signal for the train position detection. In this paper a new algorithm is proposed which uses signal's phase difference of arrival to detect the train position. Experimental verification of the algorithm is presented in the paper.

• International Journal of Railway
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• v.6 no.1
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• pp.1-6
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• 2013

Electric railway system driving the electric cars using power from catenary has been secured by performance of stable tracking between pantograph and catenary. The performance of the power collecting of pantograph is the one of the most important skills for high-speed train speed. The first Korea high-speed train(KTX) is 20 cars in one train set. In the meantime, collecting capability of single pantograph collector at one train set was confirmed through evaluation of the performance and the stability test. However, more research is needed to build for a stable collecting capability of coupled Korea's KTX-II High-speed system which is developed in Korea. In this study, actual vehicle test of coupled KTXSanchon was made to analyzing the data presented by the dynamic nature of catenary and pantograph, and the interface characteristics.

• Proceedings of the KSR Conference
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• 2002.10a
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• pp.566-573
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• 2002

In this paper, is propose to the object(human) dection method using geometrical structures and projection histograms in the image. The problem of existing methods for objects tracking of background subtracted is resulted from uncertainty at background unfixed. In this paper, two methods are applied to solve problem. This problems are proved by method. This problems is demonstrated by using this methods and applied to the train railroad crossing. Therefore, this paper aims that this contributes to improve the accident of the train railroad crossing.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.711-719
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• 2010

Heliostat, as a concentrator reflecting the incident solar energy to the receiver located at the tower, is the most important system in the tower-type solar thermal power plant, since it determines the efficiency and performance of solar thermal plower plant. Thus, a good sun tracking ability as well as its good optical property are required. In this paper, we propose a method to compensate the heliostat sun tracking error. We first model the sun tracking error, which could be measured using BCS (Beam Characterization System), by multilayered neural network. Then the extended Kalman filter was employed to train the neural network. Finally the model is used to compensate the sun tracking errors. Simulated result shows that the method proposed in this paper improve the heliostat sun tracking performance dramatically. It also shows that the training of neural network by the extended Kalman filter provides faster convergence property, more accurate estimation and higher measurement noise rejection ability compared with the other training methods like gradient descent method.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.186-196
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• 2018

In this paper, we propose an on-line tracking method using convolutional neural network (CNN) for tracking object. It is well known that a large number of training samples are needed to train the model offline. To solve this problem, we use an untrained model and update the model by collecting training samples online directly from the test sequences. While conventional methods have been used to learn models by training samples offline, we demonstrate that a small group of samples are sufficient for online object tracking. In addition, we define a loss function containing color information, and prevent the model from being trained by wrong training samples. Experiments validate that tracking performance is equivalent to four comparative methods or outperforms them.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2067-2081
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• 2024

Recently, image analysis research has been actively conducted due to the accumulation of big image data and the development of deep learning. Image analytics research has different characteristics from other data such as data size, real-time, image quality diversity, structural complexity, and security issues. In addition, a large amount of data is required to effectively analyze images with deep-learning models. However, in many fields, the data that can be collected is limited, so there is a need for meta learning based image analysis technology that can effectively train models with a small amount of data. This paper presents a comprehensive survey of meta-learning-based object-tracking techniques. This approach comprehensively explores object tracking methods and research that can achieve high performance in data-limited situations, including key challenges and future directions. It provides useful information for researchers in the field and can provide insights into future research directions.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.940-945
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• 2009

This paper presents the design method of low order controller for the active pantograph of electric train system. The pantograph is the most playa role to supply constant current to the train. The design objectives are to have good tracking performance about reference contact force despite the stiffness variation that is like sinusoidal function concerned in train speed or span length of contact wire. In this paper, we consider stiffness variation from external disturbance of active pantograph to simplify model equation, and propose simple second-order controller which is designed by Characteristic ratio assignment(CRA) control method. Finally, we verify time response appling to model equation of real system and frequency response about parameter uncertainty like stiffness variation. it is performed by Matlab version 6.5 and Matlab simulink simulation.