• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.16-22
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• 2008

Recently, automatic parking assist systems are commercially available in some cars. In order to improve the reliability and the accuracy of parking control, pose uncertainty of a vehicle and some experimental issues should be solved. In this paper, following three schemes are proposed. (1) Odometry calibration scheme for the Car-Like Mobile Robot.(CLMR) (2) Accurate localization using Extended Kalman Filter(EKF) based redundant odometry fusion. (3) Trajectory tracking controller to compensate the tracking error of the CLMR. The proposed schemes are experimentally verified using a miniature Car-Like Mobile Robot. This paper shows that odometry accuracy and trajectory tracking performance can be dramatically improved by using the proposed schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.1964-1981
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• 2014

An improved Mean-Shift (MS) tracker called joint CB-LBWH, which uses a combined weighted-histogram scheme of CBWH (Corrected Background-Weighted Histogram) and LBWH (likelihood-based Background-Weighted Histogram), is presented. Joint CB-LBWH is based on the notion that target representation employs both feature saliency and confidence to form a compound weighted histogram criterion. As the more prominent and confident features mean more significant for tracking the target, the tuned histogram by joint CB-LBWH can reduce the interference of background in target localization effectively. Comparative experimental results show that the proposed joint CB-LBWH scheme can significantly improve the efficiency and robustness of MS tracker when heavy occlusions and complex scenes exist.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.883-887
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• 2016

Navigating an unknown environment is a challenging task for a robot, especially when a large number of obstacles exist and the odometry lacks reliability. Pose tracking allows the robot to determine its location relative to its previous location. The ICP (iterative closest point) has been a powerful method for matching two point clouds and determining the transformation matrix between the maps. However, in a situation where odometry is not available and the robot moves far from its original location, the ICP fails to calculate the exact displacement. In this paper, we suggest a method that is able to match two different point clouds taken a long distance apart. Without using any odometry information, it only exploits the features of corner points containing information on the surroundings. The algorithm is fast enough to run in real time.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.177-178
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• 2006

The real-time tracking system is an essential factor for the development of low cost sensor networks for use in pervasive computing and ubiquitous networking. In this paper, we address the interference problems of the sensor network platform with ultrasonic for location tracking system. Ubiquitous indoor environments often contain substantial amounts of metal and other such reflective materials that affect the propagation of radio frequency signals in non-trivial ways, causing severe multi-path effects, dead-spots, noise, and interference. Especially we present a novel reducing interference location system that is particularly well suited to support context-aware computing. The system called Pharos, aims to combine the advantages of real-time tracking systems that implement distributed environment with regardless of infrastructure or infrastructure-less wireless sensor networks.

• Journal of Platform Technology
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• v.6 no.4
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• pp.18-25
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• 2018

This paper describes a real-time human and robot tracking method in Intelligent Space with multi-camera networks. The proposed method detects candidates for humans and robots by using the histogram of oriented gradients (HOG) feature in an image. To classify humans and robots from the candidates in real time, we apply cascaded structure to constructing a strong classifier which consists of many weak classifiers as follows: a linear support vector machine (SVM) and a radial-basis function (RBF) SVM. By using the multiple view geometry, the method estimates the 3D position of humans and robots from their 2D coordinates on image coordinate system, and tracks their positions by using stochastic approach. To test the performance of the method, humans and robots are asked to move according to given rectangular and circular paths. Experimental results show that the proposed method is able to reduce the localization error and be good for a practical application of human-centered services in the Intelligent Space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.736-752
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• 2010

We propose a novel collision-avoidance algorithm for the active type RFID regarding an indoor tracking system. Several well-known collision avoidance algorithms are analyzed considering the adequacy for the indoor tracking system. We prove the superiority of the slotted ALOHA in comparison with CSMA for short and fixed length packets like an ID message in RFID. Observed results show that they are not applicable for active type RFID in terms of energy efficiency. Putting these all together, we propose a dedicated collision avoidance algorithm considering the unique features of the indoor tracking system. The proposed method includes a scheduled tag access period (STAP) as well as a random tag access period (RTAP) to address both of the static and dynamic characteristics of the system. The system parameters are determined through a quantitative analysis of the throughput and energy efficiency. Especially, some mathematical techniques have been deployed to obtain the optimal slot count for RTAP. Finally, simulation results are provided to illustrate the performance of the proposed method with variations of the parameters.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.150-157
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• 2009

In this paper, we propose a person tracking based multi-channel audio panning system for multi-view broadcasting services. Multi-view broadcasting is to render the video sequences that are captured from a set of cameras based on different viewpoints, and multi-channel audio panning techniques are necessary for audio rendering in these services. In order to apply such a realistic audio technique to this multi-view broadcasting service, person tracking techniques which are to estimate the position of users are also necessary. For these reasons, proposed methods are composed of two parts. The first part is a person tracking method by using ultrasonic satellites and receiver. We could obtain user's coordinates of high resolution and short duration about 10 mm and 150 ms. The second part is MPEG Surround parameter-based multi-channel audio panning method. It is a method to obtain panned multi-channel audio by controlling the MPEG Surround spatial parameters. A MUSHRA test is conducted to objectively evaluate the perceptual quality and measure localization performance using a dummy head. From the experiments, it is shown that the proposed method provides better perceptual quality and localization performance than the conventional parameter-based audio panning method. In addition, we implement the prototype of person tracking based multi-view broadcasting system by integrating proposed methods with multi-view display system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1400-1420
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• 2012

Object detection and tracking using visual sensors is a critical component of surveillance systems, which presents many challenges. This paper addresses the enhancement of object detection and tracking via the combination of multiple visual sensors. The enhancement method we introduce compensates for missed object detection based on the partial detection of objects by multiple visual sensors. When one detects an object or more visual sensors, the detected object's local positions transformed into a global object position. Local and global information exchange allows a missed local object's position to recover. However, the exchange of the information may degrade the detection and tracking performance by incorrectly recovering the local object position, which propagated by false object detection. Furthermore, local object positions corresponding to an identical object can transformed into nonequivalent global object positions because of detection uncertainty such as shadows or other artifacts. We improved the performance by preventing the propagation of false object detection. In addition, we present an evaluation method for the final global object position. The proposed method analyzed and evaluated using case studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1162-1179
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• 2017

In this paper, we propose indoor location tracking method using RSSI(Received Signal Strength Indicator) value received from BLE(Bluetooth Low Energy) beacon. Due to the influence of various external environmental factors, it is very difficult to improve the accuracy in indoor location tracking. In order to solve this problem, we propose a novel method of reducing the noise generated in the external environment by using a double Gaussian filter. In addition, the value of the RSSI signal generated in the BLE beacon is different for each device. In this study, we propose a method to allocate additional weights in order to compensate the intensity of signal generated in each device. This makes it possible to improve the accuracy of indoor location tracking using beacons. The experiment results show that the proposed method effectively decrease the RSSI deviation and increase location accuracy. In order to verify the usefulness of this study, we compared the Kalman filter algorithm which is widely used in signal processing. We further performed additional experiments for application area for indoor location service and find that the proposed scheme is useful for BLE-based indoor location service.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.465-471
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• 2007

Many hospitals have been considering new technology such as wireless sensor network(WSN). The technology can be used to track the location of medical devices needed for inspections or repairs, and it can also be used to detect of a theft of an asset. In an asset-tracking system using WSN, acquiring the location of moving sensor nodes inherently introduces uncertainty in location determination. In fact, the sensor nodes attached to an asset are prone to failure from lack of energy or from physical destruction. Therefore, even if the asset is located within the predetermined area, the asset-tracking application could "misunderstand" that an asset has escaped from the area. This paper classifies the causes of such unexpected situations into the following five cases: 1) an asset has actually escaped from a predetermined area; 2) a sensor node was broken; 3) the battery for the sensor node was totally discharged; 4) an asset went into a shadow area; 5) a sensor node was stolen. We implemented and installed our asset-tracking system in a hospital and continuously monitored the status of assets such as ventilators, syringe pumps, wheel chairs and IV poles. Based on this real experience, we suggest how to differentiate each case of location uncertainty and propose possible solutions to prevent them.