• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.92-102
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• 2017

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system's data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.

• Journal of Advanced Navigation Technology
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• v.19 no.1
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• pp.74-79
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• 2015

In this paper, in order to improve the error is utilized to location tracking the smart sensor detects a walking information user, RSSI is to provide an indoor position tracking system that is capable of correcting an error in terms weak. The acceleration sensor is able to detect the activity in the user walking and detects the number of step and the moving distance using the same. The Direction sensor is utilized as a digital compass, to detect the moving direction of the user. As a result of detecting the walking information using the sensor, it can be showed that this proposed indoor positioning system has a high degree of accuracy for the number of steps and the movement direction. Therefore, this paper shows that the proposed technique can correct the error of the location information to be problem in the conventional indoor location system which uses the only Wi-Fi APs by estimating the user's movement direction and distance using the sensors in smartphone without an additional equipment and cost.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.41-49
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• 2024

This paper discusses the modeling and implementation of a worker location system at a shipbuilding site. The importance of worker location in an industrial environment is highlighted as a critical element in the prevention of industrial accidents. The paper presents a worker tracking system that integrates power line and beacon communication to accurately track worker position. Through experiments, the paper demonstrates how to monitor the changes in worker location based on different scenarios and how to access the status of worker location using the manager's web service. The paper can be used for the design of a system that will provide real-time location information to safety managers for the improvement of worker safety management.

• Korean Journal of Applied Biomechanics
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• v.17 no.1
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• pp.81-90
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• 2007

The effort identifying positioning information of the moving object in real time has been a issue not only in sport biomechanics but also other academic areas. In order to solve this issue, this study tried to track the movement of a pitched ball that might provide an easier prediction because of a clear focus and simple movement of the object. Machine learning has been leading the research of extracting information from continuous images such as object tracking. Though the rule-based methods in artificial intelligence prevailed for decades, it has evolved into the methods of statistical approach that finds the maximum a posterior location in the image. The development of machine learning, accompanied by the development of recording technology and computational power of computer, made it possible to extract the trajectory of pitched baseball from recorded images. We present a method of baseball tracking, based on object tracking methods in machine learning. We introduce three state-of-the-art researches regarding the object tracking and show how we can combine these researches to yield a novel engine that finds trajectory from continuous pitching images. The first research is about mean shift method which finds the mode of a supposed continuous distribution from a set of data. The second research is about the research that explains how we can find the mode and object region effectively when we are given the previous image's location of object and the region. The third is about the research of representing data into features that we can deal with. From those features, we can establish a distribution to generate a set of data for mean shift. In this paper, we combine three works to track baseball's location in the continuous image frames. From the information of locations from two sets of images, we can reconstruct the real 3-D trajectory of pitched ball. We show how this works in real pitching images.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.568-573
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• 2003

This paper proposed to recognize and tracking the ID and location when a person and objects moved from indoor using RFID a type of passive tag. An antenna was installed in both sides of a door due to the limitation of a recognition distance. And frequency bandwidth was used to the 134.2kHz bandwidth to pass and bend several obstacles. Because a type of passive tag is a semi -permanent and miniaturization, it has applied to this paper.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.31-39
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• 2006

Ubiquitous and pervasive environment presents opportunities for a rich set of location aware applications such as car navigation and intelligent robots, interactive virtual games, logistics service, asset tracking etc. Typical indoor location systems require better accuracy than what current outdoor location systems provide, Outdoor location technologies such as GPS have poor indoor performance because of the harsh nature of indoor environments, In this paper we present a novel reducing interference location system that is particularly well suited to support context aware computing. The system, called EEM (Enhance Envelop Method) alms to combine the advantages of real time tracking systems that implement distributed environment with the suitability of infrastructure sensor network.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.865-870
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• 2004

Development of new information and traffic technology causes fast-growing in the field of information-based system. At recent, development of LBS(Location Based Service) makes a remarkable growth of industry as GPS(Global Positioning System) becomes wide-spread and location information becomes more important. However, there is a problem like infringement of privacy when location information is used improperly［1］. In this paper, LBS platform is proposed in order to prevent infringement of privacy. To implement, we classify user path as pattern in a zone of user life. Thereupon, location information is provided according to user' specific situation.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.619-628
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• 2013

In this paper, we describe a strongly improved tracking method using combination of CAMshift and Kalman filter algorithm. CAMshift algorithm doesn't consider the object's moving direction and velocity information when it set the search windows for tracking. However if Kalman filter is combined with CAMshift for setting the search window, it can accurately predict the object's location with the object's present location and velocity information. By using this prediction before CAMshift algorithm, we can track fast moving objects successfully. Also in this research, we show better tracking results than conventional approaches which make use of single color information by using both color information of HSV and YCrCb simultaneously. This modified approach obtains more robust color segmentation than others using single color information.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.55-61
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• 2017

In this paper, we propose a novel tracking method using target separation and detection that are based on discriminative correlation filter (DCF), which is studied a lot recently. 'Retainability' is one of the most important factor of tracking. There are some factors making retainability of tracking worse. Especially, fast movement and occlusion of a target frequently occur in image data, and when it happens, it would make target lost. As a result, the tracking cannot be retained. For maintaining a robust tracking, in this paper, separation of a target is used so that normal tracking is maintained even though some part of a target is occluded. The detection algorithm is executed and find new location of the target when the target gets out of tracking range due to occlusion of whole part of a target or fast movement speed of a target. A variety of experiments with various image data sets are conducted. The algorithm proposed in this paper showed better performance than other conventional algorithms when fast movement and occlusion of a target occur.