• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.167-177
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• 2021

In the field of augmented reality, edge-based methods have been popularly used in tracking textureless 3D objects. However, edge-based methods are inherently vulnerable to cluttered backgrounds. Another way to track textureless or poorly-textured 3D objects is to directly align image intensity of 3D object between consecutive frames. Although the direct methods enable more reliable and stable tracking compared to using local features such as edges, they are more sensitive to occlusion and less accurate than the edge-based methods. Therefore, we propose a method that combines an edge-based method and a direct method to leverage the advantages from each approach. Experimental results show that the proposed method is much robust to both fast camera (or object) movements and occlusion while still working in real time at a frame rate of 18 Hz. The tracking success rate and tracking accuracy were improved by up to 84% and 1.4 pixels, respectively, compared to using the edge-based method or the direct method solely.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.3
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• pp.200-205
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• 2022

This paper presents an improved maximum power point tracking method that can fast track the global maximum power point (GMPP) for a photovoltaic system under partial shading conditions. The proposed method combines the advantages of the maximum power trapezium (MPT) method and the search-skip-judge method to minimize the tracking voltage intervals. Thus, the proposed method can quickly track the GMPP by skipping unnecessary tracking voltage intervals. The superiority of the proposed method is verified through simulation results in the MATLAB/Simulink and experimental real-time operation results with the hardware-in-the-loop simulation. The simulation and experimental results demonstrated that the proposed method has a faster tracking time than the MPT method under various partial shading conditions.

• Journal of KIBIM
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• v.12 no.4
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• pp.1-18
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• 2022

New Fabrication Facility (FAB) construction is performed with Building Information Modeling (BIM) based design. The BIM design data keep updated during the FAB construction. To improve fast-track construction management, a Fabrication Facility Augmented Reality (FABAR) was developed. This study introduces a FABAR system development process and shows performance evaluation results of the FABAR prototype system. The FABAR is implemented with three major modules: Augmented Reality (AR) visualization unit (Room-box) to transfer big BIM data to AR data, AR registration and tracking unit to match AR with real scape and to keep AR coordination in real, and AR data management unit to enhance usability. The prototype performance results were as follows: visualization of design BIM data via AR within 24 hours, precise AR registration and tracking registration, and appropriate usability to support FAB construction management at site. The results indicate that the FABAR is applicable for FAB construction management. Especially, the BIM data transformation method using Room-box in this study signifies a new construction management approach using fluctuating BIM design data in the fast track construction condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.840-860
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• 2023

Aiming at the serious occlusion and slow tracking speed in pedestrian target tracking and recognition in complex scenes, a target tracking method based on improved YOLO v5 combined with Deep SORT is proposed. By merging the attention mechanism ECA-Net with the Neck part of the YOLO v5 network, using the CIoU loss function and the method of CIoU non-maximum value suppression, connecting the Deep SORT model using Shuffle Net V2 as the appearance feature extraction network to achieve lightweight and fast speed tracking and the purpose of improving tracking under occlusion. A large number of experiments show that the improved YOLO v5 increases the average precision by 1.3% compared with other algorithms. The improved tracking model, MOTA reaches 54.3% on the MOT17 pedestrian tracking data, and the tracking accuracy is 3.7% higher than the related algorithms and The model presented in this paper improves the FPS by nearly 5 on the fps indicator.

• Journal of the Korea Society of Computer and Information
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• v.7 no.1
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• pp.33-47
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• 2002

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1477-1484
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• 2015

This paper proposes a robust method for object tracking based on Kalman filters algorithm and covariance matrix. As a feature of the object to be tracked, covariance matrix ensures the continuity of the moving target tracking in the image frames because the covariance is addressed spatial and statistical properties as well as the correlation properties of the features, despite the changes of the form and shape of the target. However, if object moves faster than operation time, real time tracking is difficult. In order to solve the problem, Kalman filters are used to estimate the area of the moving object and covariance matrices as a feature vector are compared with candidate regions within the estimated Kalman window. The results show that the tracking rate of 96.3% achieved using the proposed method.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.61-68
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• 2016

Surveillance cameras have been installed in many places because security and safety has become an important issue in modern society. However, watching surveillance videos and judging accidental situations is very labor-intensive and time-consuming. So now, requests for research to automatically analyze the surveillance videos is growing. In this paper, we propose a surveillance system to track multiple persons in videos and to summarize the videos based on tracking information. The proposed surveillance summarization system applies an adaptive illumination correction, subtracts the background, detects multiple persons, tracks the persons, and saves their tracking information in a database. The tracking information includes tracking one's path, their movement status, length of staying time at the location, enterance/exit times, and so on. The movement status is classified into six statuses(Enter, Stay, Slow, Normal, Fast, and Exit). This proposed summarization system provides a person's status as a graph in time and space and helps to quickly determine the status of the tracked person.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.52-55
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• 1997

This paper discusses an automatic visual-servoing system, in which a laser and a CCD camera are used for imaging the pattern of joint groove. The algorithm used here is simple and robust to find out the gap width and gap center. As a consequence, the speed of algorithm is very fast and optimized. A feature of this system is that it processes only by summing the vertical line and horizontal line of screen without any image preprocessing in order to get the energy information of lines alternatively. It is practical and useful for the system requiring a fast process time of vision.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.38-41
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• 1991

RLS algorithms are required for applications to adaptive line enhancers, adaptive equalizers for voiceband telephone and HF modems, and wide-badn digital spectrum mobile raio in which their convergence time and tracking speed are significant. The fast QR RLS algorithm satisfies above the requirements. Its computational complexity is linearly proportional to the tap number of a filter, N and its performance remains numerically stable. From the result of simumulation, the fast QR RLS algorithm represented Cioffi is better than gradient based algorithm in its initial performance when being applied to an adaptive line enhancer for cancelling noise.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.345-354
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• 2010

Visual tracking techniques for Augmented Reality are classified as either a marker tracking approach or a natural feature tracking approach. Marker-based tracking algorithms can be efficiently implemented sufficient to work in real-time on mobile devices. On the other hand, natural feature tracking methods require a lot of computationally expensive procedures. Most previous natural feature tracking methods include heavy feature extraction and pattern matching procedures for each of the input image frame. It is difficult to implement real-time augmented reality applications including the capability of natural feature tracking on low performance devices. The required computational time cost is also in proportion to the number of patterns to be matched. To speed up the natural feature tracking process, we propose a novel fast tracking method based on optical flow. We implemented the proposed method on mobile devices to run in real-time and be appropriately used with mobile augmented reality applications. Moreover, during tracking, we keep up the total number of feature points by inserting new feature points proportional to the number of vanished feature points. Experimental results showed that the proposed method reduces the computational cost and also stabilizes the camera pose estimation results.