• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.103-106
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• 2003

In this paper, we present a hybrid inertial and vision-based tracking system for VR applications. One of the most important aspects of VR (Virtual Reality) is providing a correspondence between the physical and virtual world. As a result, accurate and real-time tracking of an object's position and orientation is a prerequisite for many applications in the Virtual Environments. Pure vision-based tracking has low jitter and high accuracy but cannot guarantee real-time pose recovery under all circumstances. Pure inertial tracking has high update rates and full 6DOF recovery but lacks long-term stability due to sensor noise. In order to overcome the individual drawbacks and to build better tracking system, we introduce the fusion of vision-based and inertial tracking. Sensor fusion makes the proposal tracking system robust, fast, accurate, and low jitter and noise. Hybrid tracking is implemented with Kalman Filter that operates in a predictor-corrector manner. Combining bluetooth serial communication module gives the system a full mobility and makes the system affordable, lightweight energy-efficient. and practical. Full 6DOF recovery and the full mobility of proposal system enable the user to interact with mobile device like PDA and provide the user with natural interface.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.355-362
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• 2010

Face tracking is to estimate the motion of a non-rigid face together with a rigid head in 3D, and plays important roles in higher levels such as face/facial expression/emotion recognition. In this paper, we propose an AAM-based face tracking algorithm. AAM has been widely used to segment and track deformable objects, but there are still many difficulties. Particularly, it often tends to diverge or converge into local minima when a target object is self-occluded, partially or completely occluded. To address this problem, we utilize the scale invariant feature transform (SIFT). SIFT is an effective method for self and partial occlusion because it is able to find correspondence between feature points under partial loss. And it enables an AAM to continue to track without re-initialization in complete occlusions thanks to the good performance of global matching. We also register and use the SIFT features extracted from multi-view face images during tracking to effectively track a face across large pose changes. Our proposed algorithm is validated by comparing other algorithms under the above 3 kinds of occlusions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.177-179
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• 2021

This paper proposes a method of estimating location of a target point at which an interactive robot gazes in an indoor space. RGB images are extracted from low-cost web-cams, user head pose is obtained from the face detection (Openface) module, and geometric configurations are applied to estimate the user's gaze direction in the 3D space. The coordinates of the target point at which the user stares are finally measured through the correlation between the estimated gaze direction and the plane on the table plane.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.871-891
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• 2019

Visual tracking is a challenging task that needs learning an effective model to handle the changes of target appearance caused by factors such as pose variation, illumination change, occlusion and motion blur. In this paper, a novel tracking algorithm based on weighted local sub-space reconstruction error is presented. First, accounting for the appearance changes in the tracking process, a generative weight calculation method based on structural reconstruction error is proposed. Furthermore, a template update scheme of occlusion-aware is introduced, in which we reconstruct a new template instead of simply exploiting the best observation for template update. The effectiveness and feasibility of the proposed algorithm are verified by comparing it with some state-of-the-art algorithms quantitatively and qualitatively.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.1
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• pp.30-37
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• 2021

Accurate 3D object tracking with camera images is a key enabling technology for augmented reality applications. Motivated by the impressive success of convolutional neural networks (CNNs) in computer vision tasks such as image classification, object detection, image segmentation, recent studies for 3D object tracking have focused on leveraging deep learning. In this paper, we review deep learning approaches for 3D object tracking. We describe key methods in this field and discuss potential future research directions.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.20-28
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• 2015

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.363-368
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• 2007

Most surgery illumination systems have been developed as passive systems. However, sometimes it is inconvenient to relocate the position of the illumination system whenever the surgeon changes his pose. To cope with such a problem, this study develops an auto-illumination system that is autonomously tracking the surgeon's movement. A 5-DOF serial type manipulator system that can control (X, Y, Z, Yaw, Pitch) position and secure enough workspace is developed. Using 3 ultrasonic sensors, the surgeon's position and orientation could be located. The measured data aresent to the main control system so that the robot can be auto-tracking the target. Finally, performance of the developed auto-illuminating system was verified through a preliminary experiment in the operating room environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.121-126
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• 2017

Tracking system is essential for Image Guided Surgery(IGS). Optical Tracking System(OTS) is widely used to IGS for its high accuracy and easy usage. However, OTS doesn't work when occlusion of marker occurs. In this paper sensor data fusion with OTS and Inertial Navigation System(INS) is proposed to solve this problem. The proposed system improves the accuracy of tracking system by eliminating gaussian error of the sensor and supplements the disadvantages of OTS and IMU through sensor fusion based on Kalman filter. Also, sensor calibration method that improves the accuracy is introduced. The performed experiment verifies the effectualness of the proposed algorithm.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.67-90
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• 2019

Accurate detection, tracking and analysis of human movement using robots and other visual surveillance systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which involved scanning of various sizes of windows across an image. This paper concentrates on employing a state-of-the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme with validation phase. Localization of human region in each frame is performed by keypoints by casting votes for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based framework is used to detect voting behavior. The designed methodology is tested on the video sequences having 3 to 4 persons.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.167-177
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• 2019

Even though so much progresses have been achieved in Multiple Object Tracking (MOT), most of reported MOT methods are not still satisfactory for commercial embedded products like Pan-Tilt-Zoom (PTZ) camera. In this paper, we propose a real-time multiple pedestrians tracking method for embedded environments. First, we design a new light weight convolutional neural network(CNN)-based pedestrian detector, which is constructed to detect even small size pedestrians, as well. For further saving of processing time, the designed detector is applied for every other frame, and Kalman filter is employed to predict pedestrians' positions in frames where the designed CNN-based detector is not applied. The pose orientation information is incorporated to enhance object association for tracking pedestrians without further computational cost. Through experiments on Nvidia's embedded computing board, Jetson TX2, it is verified that the designed pedestrian detector detects even small size pedestrians fast and well, compared to many state-of-the-art detectors, and that the proposed tracking method can track pedestrians in real-time and show accuracy performance comparably to performances of many state-of-the-art tracking methods, which do not target for operation in embedded systems.