• 인터넷정보학회논문지
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• 제16권5호
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• pp.93-98
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• 2015

최근 들어서 정보통신기술 자체뿐만 사물인터넷(IoT)을 활용한 비즈니스가 빠르게 확대되고 있다. 일반적으로 사물인터넷 중에서 웨어러블 디바이스는 고객을 대상으로 활용된 분야 중 가장 선도적인 적용분야로 인식되고 있다. 본 연구는 먼저 웨어러블 디바이스의 대표적 응용분야인 활동량 측정기에 대해서 고객의 니즈를 파악하고, 이를 잘 알려진 마케팅 믹스(제품, 가격, 유통, 촉진)와 연계시킨다. 이를 위해서 대학생들을 대상으로 서베이가 수행되었으며, 활동량 측정기의 현재 및 잠재된 니즈를 밝혔다. 또한 이러한 니즈는 마케팅 믹스에 의해서 분류되었다. 활동량 측정기는 다른 정보통신기기와는 달리 잠재된 욕구가 많았으며, 의료기기적 특성으로 인해 유통채널 등도 온라인, 대리점이 아닌 신뢰성을 보장할 직영점이나 오프라인 매장의 선호도가 크다. 본 연구결과는 일반 사물인터넷으로서 활동량 측정기뿐 아니라 웨어러블 디바이스를 개발하려는 개발자와 경영층에게 디자인요소에 관한 새로운 시사점을 제공할 것이다.

• 대한원격탐사학회지
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• 제31권4호
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• pp.353-360
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• 2015

Earth's gravity field recovery was improved after the pre-processing upgrade of the Gravity Recovery And Climate Experiments (GRACE) satellite's star trackers. The star tracker measurements were filtered with a tighter low-pass filtering of 0.025Hz cutoff frequency, instead of a nominal filtering of 0.1Hz cutoff frequency. In addition, a jump removal algorithm was applied to remove discontinuities, due to direct Sun and/or Moon interventions, in the star tracker measurements. During the K-Band Ranging (KBR) calibration maneuvers, large attitude variations could be detected concurrently by both of the star trackers and the accelerometer. The misalignment angles of star trackers between the true frame and the normal frame could be determined by comparing measurements from these sensors. In this paper, new Earth' gravity field maps were obtained using above improvement. Based on comparisons to nominal Earth's gravity field maps, the new Earth's gravity field maps were found better than the nominal ones. Among the applied methods, the misalignment calibration of the star trackers had a major impact on the improvement of the new Earth's gravity field maps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권1호
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• pp.201-220
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• 2020

Trackers, especially long-term (LT) trackers, now have a more complex structure and more intensive computation for nowadays' endless pursuit of high accuracy and robustness. However, computing efficiency of LT trackers cannot meet the real-time requirement in various real application scenarios. Considering heterogeneous CPU-GPU platforms have been more popular than ever, it is a challenge to exploit the computing capacity of heterogeneous platform to improve the efficiency of LT trackers for real-time requirement. This paper focuses on TLD, which is the first LT tracking framework, and proposes an efficient parallel implementation based on OpenCL. In this paper, we firstly make an analysis of the TLD tracker and then optimize the computing intensive kernels, including Fern Feature Extraction, Fern Classification, NCC Calculation, Overlaps Calculation, Positive and Negative Samples Extraction. Experimental results demonstrate that our efficient parallel TLD tracker outperforms the original TLD, achieving the 3.92 speedup on CPU and GPU. Moreover, the parallel TLD tracker can run 52.9 frames per second and meet the real-time requirement.

• 항공우주기술
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• 제11권2호
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• pp.87-95
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• 2012

본 기술논문에서는 고기동 고정밀 위성의 별 추적기 초기 점검 원격측정 데이터가 분석되었다. 최근에 발사된 위성은 고기동 고정밀 위성이므로 고도의 요구조건을 만족하기 위해 광학계, 전자부, 배플이 분리된 형태로 광학계 부분의 온도를 20 degC 근처로 유지하면서 저주파 오차 성능을 향상 시킨 프랑스 SODERN사의 SED36을 사용하였다. 본 논문에서는 위성으로부터 내려 받은 원격측정 데이터의 처리를 통해 초기 점검 상태, 요구 조건 만족 여부, 이상 현상 및 주요 임무 기동에서의 별 추적기 잡음각 성능 분석 결과를 제시하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3976-3990
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• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.

• ETRI Journal
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• 제42권1호
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• pp.54-66
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• 2020

Nowadays, the broad availability of cameras and embedded systems makes the application of computer vision very promising as a supporting technology for intelligent transportation systems, particularly in the field of vehicle tracking. Although there are several existing trackers, the limitation of using low-cost cameras, besides the relatively low processing power in embedded systems, makes most of these trackers useless. For the tracker to work under those conditions, the video frame rate must be reduced to decrease the burden on computation. However, doing this will make the vehicle seem to move faster on the observer's side. This phenomenon is called the fast motion challenge. This paper proposes a tracker called dynamic swarm particle (DSP), which solves the challenge. The term particle refers to the particle filter, while the term swarm refers to particle swarm optimization (PSO). The fundamental concept of our method is to exploit the continuity of vehicle dynamic motions by creating dynamic models based on PSO. Based on the experiments, DSP achieves a precision of 0.896 and success rate of 0.755. These results are better than those obtained by several other benchmark trackers.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2008년도 한국우주과학회보 제17권2호
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• pp.28.2-28.2
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• 2008

Recently, star sensors have been successfully used as main attitude sensors for attitude control in many satellites. This research presents the star visibility analysis for star trackers and the goal of this analysis is to make sure that the star tracker implementation is suitable to the mission profile and scenario and satisfies the requirement of attitude orbit control system. As a main optical attitude sensor imaging stars, accomodations of a star tracker should be optimized in order to improve the probability of the usage by avoiding the blinding (the unavailability) by the Sun and the Earth. For the analysis, a statistical approach and a time simulation approach are used. The statistical approach is based on the generation of numerous cases, to derive relevant statistics about Earth and Sun proximity probabilites for different lines of sight. The time simulation approach is performed for one orbit to check the statistical result and to refine the statistical result and accomodations of star trackers. In order to perform simulations first of all, an orbit and specific mission profiles of a satellite are set, next the earth proximity probability and the sun proximity probability are calculated by considering the attitude maneuvers and the geometry of the orbit, and then finally the unavailability positions are estimated. As a result, the optimized accomodations of two star trackers are suggested for the low earth orbit satellite.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1121-1141
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• 2020

We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.805-825
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• 2017

Correlation Filter-based Trackers (CFTs) gained popularity recently for their effectiveness and efficiency. To deal with the size changes of the target which may degenerate the tracking performance, scale estimation has been introduced in existing CFTs. However, the variations of the aspect ratio were usually neglected, which also influence the size of the target. In this paper, Size Aware Correlation Filter Trackers (SACFTs) are proposed to deal with this problem. The SACFTs not only determine the translation and scale variations, but also take the aspect ratio changes into consideration, thus a better estimation of the size of the target can be realized, which improves the overall tracking performance. And competing results can be achieved compared with state-of-the-art methods according to the experiments conducted on two large scale datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5161-5178
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• 2019

Correlation Filter was recently demonstrated to have good characteristics in the field of video object tracking. The advantages of Correlation Filter based trackers are reflected in the high accuracy and robustness it provides while maintaining a high speed. However, there are still some necessary improvements that should be made. First, most trackers cannot handle multi-scale problems. To solve this problem, our algorithm combines position estimation with scale estimation. The difference from the traditional method in regard to the scale estimation is that, the proposed method can track the scale of the object more quickly and effective. Additionally, in the feature extraction module, the feature representation of traditional algorithms is relatively simple, and furthermore, the tracking performance is easily affected in complex scenarios. In this paper, we design a novel and powerful feature that can significantly improve the tracking performance. Finally, traditional trackers often suffer from model drift, which is caused by occlusion and other complex scenarios. We introduce a relocation component to detect object at other locations such as the secondary peak of the response map. It partly alleviates the model drift problem.