• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1828-1836
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• 2015

Object tracking algorithms can reduce computational cost by avoiding computation over the whole image through the selection of region of interests based on object detection. So, accurate object detection is an important task for object tracking. The background subtraction algorithm has been widely used in moving object detection using a stationary camera. However, it has the problem of object detection error due to incorrect background modeling, whereas the method of background modeling has been improved by many researches. This paper proposes a new moving object detection algorithm to overcome the drawback of the conventional background subtraction algorithm by combining the background subtraction algorithm with the motion history image algorithm that is usually used in gesture detection. Although the proposed algorithm demands more processing time because of time taken for combining two algorithms, it meet the real-time processing requirement. Moreover, experimental results show that it has higher accuracy compared with the previous two algorithms.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.125-128
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• 2004

최근 이동 통신 기술의 급속한 발달로 인해 휴대폰, PDA 등과 같은 휴대용 단말기의 사용이 보편화 되고 있다. 따라서 이동 객체의 시간에 따른 공간적인 위치정보를 활용하여 다양한 서비스를 제공하는 위치 기반 서비스(Location-Based Service)에 관한 많은 연구가 진행되고 있다. 위치 기반 서비스의 효율적 제공을 위하여 시간에 따라 지속적으로 변하는 이동 객체의 대용량 시공간 정보를 신속하게 저장, 관리, 검색할 수 있는 인덱싱 및 질의 처리 기술이 수반되어야 한다. 본 논문에서는 대용량 이동 객체 데이터베이스를 대상으로 한 효율적인 인덱스 구축을 위한 데이터 압축 표현 방식에 대하여 논한다. 이를 위해 본 논문에서는 기존의 주요 연구에서 2 차원 공간 좌표 (X, Y)로 표현되던 이동 객체의 공간 정보를 계층적 구조를 갖는 행정구역에 기반하여 1 차원의 공간 정보로 압축 표현하는 방식을 제안한다. 이를 이용하여 대용량의 공간정보를 저장하고 있는 이동 객체 데이터베이스의 인덱스 크기의 감소 및 질의 처리 시간의 향상 효과를 얻을 수 있다. 또한, 제안된 방식은 2 차원 공간 좌표를 1 차원의 행정구역 기반의 위치정보로 표현하기 때문에 이로 인한 데이터 손실이 발생할 수 있으나, 일정 공간 내의 객체분포를 필요로 하는 교통 상황 파악, 근사적(approximate) 공간 정보를 필요로 하는 사람${\cdot}$차량 위치 추적 서비스와 같은 위치 기반 서비스를 신속하게 처리하는데 유용하게 사용될 수 있다.

• Journal of Advanced Navigation Technology
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• v.21 no.1
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• pp.119-125
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• 2017

In this paper, we propose a method that merges and splits players in the MeanShift tracking framework. The MeanShift tracking moves the center of tracking window to the maximum probability location given the target probability distribution. This tracking method has been widely used for real-time tracking problems because of its fast processing speed. However, it hardly handles occlusions in multiple object tracking systems. Occlusions can be usually solved by applying data association methods. In this paper, we propose a method that can be applied before data association methods. The proposed method automatically merges and splits the overlapped players by adjusting the each player's tracking map. We have compared the tracking performance of the MeanSfhit tracking algorithm and the proposed method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.2
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• pp.82-85
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• 2014

It is helpful to use Intelligent Video Surveillance to replace and supplement the demerit which can possibly occur due to the mistake that can be made by human management. To accomplish this, it is essential that the system should digitalize image information from surveillance camera so that the system, itself, can be able to locate a object and to analyze the pattern of the object. Also, it is imperative that the system should have ability to operate a alarm and a entrance blocking system and to notify a situation to a security manager. Zooming a small object form a screen, however, requires a exact zooming ratio of the object and a shift of centric coordinate. In this paper, It is able to locate and observe closely a object from flexible background, regardless of the distance, by calculating a zooming ratio according to object moment, pan coordinate, and tilt coordinate.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.272-273
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• 2012

본 논문에서는 이동 객체가 다중 공간을 이동할 때 초기 인증 정보를 기반으로 연속적으로 인증 서비스를 제공받기 위한 방식을 제안한다. 이는 객체의 이동 경로에 설치되어 있는 스마트 카메라의 연속 추적 기능을 사용함으로써 가능하다.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.449-463
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• 2015

Video object tracking is important for variety of applications, such as security, video indexing and retrieval, video surveillance, communication, and compression. This paper proposes an object tracking method in HEVC bitstreams. Without pixel reconstruction, motion vector (MV) and size of prediction unit in the bitstream are employed in an Spatio-Temporal Markov Random Fields (ST-MRF) model which represents the spatial and temporal aspects of the object's motion. Coefficient-based object shape adjustment is proposed to solve the over-segmentation and the error propagation problems caused in other methods. In the experimental results, the proposed method provides on average precision of 86.4%, recall of 79.8% and F-measure of 81.1%. The proposed method achieves an F-measure improvement of up to 9% for over-segmented results in the other method even though it provides only average F-measure improvement of 0.2% with respect to the other method. The total processing time is 5.4ms per frame, allowing the algorithm to be applied in real-time applications.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.272-277
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• 2010

AR(Augmented Reality) needs medium between real and virtual, world, and recognition techniques are necessary to track an object continuously. Optical tracking using marker is mainly used, but it takes time and is inconvenient to attach marker onto the target objects. Therefore, many researchers try to develop markerless tracking techniques nowaday. In this paper, we extract features and 3D position from 3D objects and suggest realtime tracking based on these features and positions, which do not use just coplanar features and 2D position. We extract features using SURF, get rotation matrix and translation vector of 3D object using POSIT with these features and track the object in real time. If the extracted features are nor enough and it fail to track the object, then new features are extracted and re-matched to recover the tracking. Also, we get rotation in matrix and translation vector of 3D object using POSIT and track the object in real time.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.25-32
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• 2016

In this paper, we propose a method that is tracking an object in real time using particle filter and the observation model with edge. First of all, the proposed method defines the object to be tracked in the initial frame. Then, it generates the edge observation model for the object to be tracked and a set of particles. It calculates the weight by comparing the average of the middle distance in eight-way of particle filter edge model with that in edge observation model, and then updates the weight with the calculated value. After resampling particles using the updated weights, it estimates the current location of the tracked object. Finally, this paper demonstrates the performance of the stable tracking through comparison with the existing method by using a number of experimental data.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.209-215
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• 2015

In the past, there have been many theory and algorithms for vehicle tracking. But the time complexity of many feature point matching methods for vehicle tracking are exponential. Also, object segmentation and detection algorithms presented for vehicle tracking are exhaustive and time consuming. Therefore, we present the fast and efficient two stages method that can efficiently track the many moving vehicles on the road. The first detects the vehicle plate regions and extracts the feature points of vehicle plates. The second associates the feature points between frames using dynamic programming.

• Journal of Advanced Navigation Technology
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• v.10 no.4
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• pp.400-405
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• 2006

In this paper, we propose a moving object tracking algorithm by using motion estimation in UAV(Unmanned Aerial Vehicle) video. Proposed algorithm is based on generation of initial image from detected reference image, and tracking of moving object under the time-varying image. With a series of this procedure, tracking process is stable even when the UAV camera sways by correcting position of moving object, and tracking time is relatively reduced. A block matching algorithm is also utilized to determine the similarity between reference image and moving object. An experimental result shows that our proposed algorithm is better than the existing full search algorithm.