• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.1032-1041
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• 2007

The global navigation satellite system (GNSS), which is the core technique for the location based service, adopts the direct sequence/spread spectrum (DS/SS) as its modulation method. The success of a DS/SS system depends on the synchronization between the received and locally generated pseudo noise (PN) signals. As a step in the synchronization process, the tacking scheme performs fine adjustment to bring the phase difference between the two PN signals to zero. The most widely used tracking scheme is the delay locked loop with early minus late discriminator (EL-DLL). In the ideal case, the EL-DLL is the best estimator among various DLL. However, in the band-limited multipath environment, the EL-DLL has tracking bias. In this paper, the timing offset range of correlation function is divided into advanced offset range (AOR) and delayed offset range (DOR) centering around the correct synchronization time point. The tracking bias results from the following two reasons: symmetry distortion between correlation values in AOR and DOR, and mismatch between the time point corresponding to the maximum correlation value and the synchronization time point. The former and latter are named as the type I and type II tracking bias, respectively. In this paper, when the receiver has finite bandwidth in the presence of multipath signals, it is shown that the type II tracking bias becomes a more dominant error factor than the type I tracking bias, and the correlation values in AOR are not almost changed. Exploiting these characteristics, we propose a novel tracking bias mitigation scheme and demonstrate that the tracking accuracy of the proposed scheme is higher than that of the conventional scheme, both in the presence and absence of noise.

• Smart Media Journal
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• v.5 no.4
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• pp.90-95
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• 2016

Recently, the quality of cultural experience can be improved through a stereoscopic information service provided by the latest mobile-based Information Telecommunication technology without the human cultural commentators, which was used in order to enhance the understanding of our cultural heritage. The purpose of this paper is to produce contents that introduce cultural heritage using the Android-based GPS and augmented reality. In this paper we propose a culture content creation method that is based on location information such as user/cultural anomalies using GPS and augmented reality based on Markerless Tracking. Marker Detection Technology and Markerless Tracking Technology are used for smart phone's rapid recognition of augmented real world and accurate recognition according to the state of the cultural heritage. Also, the Google Map of Android is used to locate the user. The strength of this method lies in that it can be used for a variety of subjects while the existing methods are limited to certain kinds of augmented reality contents.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.249-256
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• 2013

Recent days, the global warming problem due to excessive use of fossil fuels has been the world wide issue so that Korea government and developed countries make an alternative plan for eco-friendly and transportations by utilizing bicycles for health promotion. Existing Public Bicycle Rental Service Systems provide simple service such as bicycle rental management and tracking the location. However there is no user-side service for the information of the measure of carbon reduction, or use of bicycles. The proposed system in this paper provides users' health information, environmental information, personal information, counseling services and the measures of carbon reduction. The new system has been developed to carry out reducing carbon and growing green in terms of using bicycles by adopting Web 2.0 technology.

• Journal of the Korea Society of Computer and Information
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• v.18 no.1
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• pp.103-110
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• 2013

RFID is estimated to have great potential for hospitals to reduce various costs, reconsider patients' safety, and provide high quality medical service. As the ubiquitous technology allows the medical market to expand, medical centers all around the country are fiercely competing against one another. In order to increase the hospital's competitive edge by reducing IT expenses and concentrating on medical practice, the need to establish a u-Hospital using advanced IT technology is rapidly rising. This paper implements an RFID based u-Hospital system by using ubiquitous computing technology to design a location tracking device for all surgical patients, which can secure the patients' safety and increase the quality of medical service through interlocking hospital information systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2463-2470
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• 2010

In some cases of u-health services, it is important and then required to pursue the location of users in real time because users could occasionally be in the situation beyond self-regulation. Location positioning has used two such typical techniques as trilateration and fingerprinting. A trilateration technique is usually based on three resident coordinates to identify the location of the target node. Ranging that measures distances between coordinates and the node is essential for applying the trilateration technique. Users especially in u-health services could be out of reachability in any such reasons as out of propagation ranges, fault of a communication counterpart, non-existence of possible communication facility, etc. This paper proposes a real-time locating method which can improve to identify and track the location of mobile objects. The method is based on references of identified mobile nodes as well as resident coordinates even though they may be partially out of communication reachability.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.75-81
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• 2012

RFID is new recognition technology which can trace IC chip(built in information) with wireless frequency. The present RFID is used in parcel service, missing child potection system, bus card, and extensive areas. This paper proposes and designs a belongings position tracking system using RFID with GPS. The main features of the proposed system are as follows. First, the system can in real time track the lost belongings. Second, the system can provide lost belonging location information to the users. The performance evaluation of the proposed system is performed by using AVD(Android Virture Device).

• Journal of KIISE:Information Networking
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• v.30 no.5
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• pp.581-594
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• 2003

Recently, a location management is being more important in mobile communication systems due to an explosive increase of mobile users. Current systems have used a concept of location area. Based on this concept, a mobile user performs a location update whenever it moves into a new location area. However, this scheme can not avoid unnecessary location updates when a mobile user moves around with high movement rate as compared to call arrival rate. That results in tremendous location management cost. To overcome this drawback, our proposal divides service areas into two sets: One is a set of areas that mobile users move with high speed and another is a set of areas that they move with low speed. After establishing these two sets, this paper employs different location tracking schemes for each sets. Generally, most mobile users with high speed have a low CMR and a regular direction until they arrive at their destination. Using such the moving behavior, systems can predict a mobile user's next location area in advance. When the mobile user moves into the predicted location, our proposal does not perform a location update. Thus, it can reduce overall location management cost. The Markov model is used to analyze the performance of our proposal. Using the model, this paper compares our proposal with IS-41 and TLA. The analytic results show that as CMR grows lower, an overall cost of our proposal becomes less, particularly if a mobile user frequently moves into the specific location are predicted by mobile systems. Also, our proposal has a better performance than other two schemes when the communication cost between HLR and VLR is high.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.546-554
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• 2019

Typically, closed-circuit television (CCTV) monitoring is mainly used for post-processes (i.e. to provide evidence after an incident has occurred), but by using a streaming video feed, machine-based learning, and advanced image recognition techniques, current technology can be extended to respond to crimes or reports of missing persons in real time. The multi-CCTV cooperation technique developed in this study is a program model that delivers similarity information about a suspect (or moving object) extracted via CCTV at one location and sent to a monitoring agent to track the selected suspect or object when he, she, or it moves out of range to another CCTV camera. To improve the operating efficiency of local government CCTV control centers, we describe here the partial automation of a CCTV control system that currently relies upon monitoring by human agents. We envisage an integrated crime prevention service, which incorporates the cooperative CCTV network suggested in this study and that can easily be experienced by citizens in ways such as determining a precise individual location in real time and providing a crime prevention service linked to smartphones and/or crime prevention/safety information.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.93-102
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• 2014

Sensor networks are utilized in various fields as the useful system that provides the useful information to the user, by storing, processing and analyzing the various data collected from sensors. In construction of such sensor networks, by utilizing cloud PaaS for both communication server and the database server, the cost reduction and stabilization of the system can be achieved. In this paper, UDIPSN (User Decision Information Providing Sensor Network) was implemented to provide the decision information to the users by being treated as a cloud service for the location information of a moving object as well as various sensor data. Finally, we showed that the operation of the proposed system was performed properly.