• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.216-223
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• 2015

The autonomous underwater vehicle (AUV) is being widely researched in order to achieve superior performance when working in hazardous environments. This research focuses on using image processing techniques to estimate the AUV's egomotion and the changes in orientation, based on image frames from different time frames captured from a single high-definition web camera attached to the bottom of the AUV. A visual odometry application is integrated with other sensors. An internal measurement unit (IMU) sensor is used to determine a correct set of answers corresponding to a homography motion equation. A pressure sensor is used to resolve image scale ambiguity. Uncertainty estimation is computed to correct drift that occurs in the system by using a Jacobian method, singular value decomposition, and backward and forward error propagation.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.313-321
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• 2017

One of the most frequently performed tasks in human-robot interaction (HRI), intelligent vehicles, and security systems is face related applications such as face recognition, facial expression recognition, driver state monitoring, and gaze estimation. In these applications, accurate head pose estimation is an important issue. However, conventional methods have been lacking in accuracy, robustness or processing speed in practical use. In this paper, we propose a novel method for estimating head pose with a monocular camera. The proposed algorithm is based on a deep neural network for multi-task learning using a small grayscale image. This network jointly detects multi-view faces and estimates head pose in hard environmental conditions such as illumination change and large pose change. The proposed framework quantitatively and qualitatively outperforms the state-of-the-art method with an average head pose mean error of less than $4.5^{\circ}$ in real-time.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.8
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• pp.156-167
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• 1995

In this paper, we propose a 3-D range finding method for situation that stereo camera has small translational motion. Binocular stereo generally tends to produce stereo correspondence errors and needs huge amount of computation. The former drawback is because the additional constraints to regularize the correspondence problem are not always true for every scene. The latter drawback is because they use either correlation or optimization to find correct disparity. We present a method which overcomes these drawbacks by moving the stereo camera actively. The method utilized a motion parallax acquired by monocular motion stereo to restrict the search range of binocular disparity. Using only the uniqueness of disparity makes it possible to find reliable binocular disparity. Experimental results with real scene are presented to demonstrate the effectiveness of this method.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.403-410
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• 2016

This paper proposes a robust image-based visual servoing scheme using a nonlinear observer for a monocular eye-in-hand manipulator. The proposed control method is divided into a range estimation phase and a target-tracking phase. In the range estimation phase, the range from the camera to the target is estimated under the non-moving target condition to solve the uncertainty of an interaction matrix. Then, in the target-tracking phase, the feature point uncertainty caused by the unknown motion of the target is estimated and feature point errors converge sufficiently near to zero through compensation for the feature point uncertainty.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.942-956
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• 2014

This paper proposes a real-time hand pose tracking method using a monocular RGB camera. Hand tracking has high ambiguity since a hand has a number of degrees of freedom. Thus, to reduce the ambiguity the proposed method adopts the step-by-step estimation scheme: a palm pose estimation, a finger yaw motion estimation, and a finger pitch motion estimation, which are performed in consecutive order. Assuming a hand to be a plane, the proposed method utilizes a planar hand model, which facilitates a hand model regeneration. The hand model regeneration modifies the hand model to fit a current user's hand, and improves robustness and accuracy of the tracking results. The proposed method can work in real-time and does not require GPU-based processing. Thus, it can be applied to various platforms including mobile devices such as Google Glass. The effectiveness and performance of the proposed method will be verified through various experiments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.267-274
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• 2009

Recently, UAVs (Unmanned Aerial Vehicles) are expected much as the Unmanned Systems for various missions. These missions are often based on the Vision System. Especially, missions such as surveillance and pursuit have a process which is carried on through the transmitted vision data from the UAV. In case of small UAVs, monocular vision is often used to consider weights and expenses. Research of missions performance using the monocular vision is continued but, actually, ground and target model have difference in distance from the UAV. So, 3D distance measurement is still incorrect. In this study, Mean-Shift Algorithm, Optical Flow and Subspace Method are posed to estimate the relative depth. Mean-Shift Algorithm is used for target tracking and determining Region of Interest (ROI). Optical Flow includes image motion information using pixel intensity. After that, Subspace Method computes the translation and rotation of image and estimates the relative depth. Finally, we present the results of this study using images obtained from the UAV experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.904-907
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• 2014

In this work we present a robust and fast approach to estimate 3D vehicle pose that can provide results under a specific traffic surveillance conditions. Such limitations are expressed by single fixed CCTV camera that is located relatively high above the ground, its pitch axes is parallel to the reference plane and the camera focus assumed to be known. The benefit of our framework that it does not require prior training, camera calibration and does not heavily rely on 3D model shape as most common technics do. Also it deals with a bad shape condition of the objects as we focused on low resolution surveillance scenes. Pose estimation task is presented as PnP problem to solve it we use well known "POSIT" algorithm [1]. In order to use this algorithm at least 4 non coplanar point's correspondence is required. To find such we propose a set of techniques based on model and scene geometry. Our framework can be applied in real time video sequence. Results for estimated vehicle pose are shown in real image scene.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.371-374
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• 1997

Recently, the development of computer achieves a system which is similar to the mechanics of human visual system. The 3-dimensional measurement using monocular vision system must be achieved a camera calibration. So far, the camera calibration technique required reference target in a scene. But, these methods are inefficient because they have many calculation procedures and difficulties in analysis. Therefore, this paper proposes a native method that without reference target in a scene. We use the grid type frame with different line widths. This method uses vanishing point concept that possess a rotation parameter of the camera and perspective ration that perspect each line widths into a image. We confirmed accuracy of calibration parameter estimation through experiment on the algorithm with a grid paper with different line widths.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2333-2335
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• 1998

Recently, the development of computer achieves a system which is similar to the mechanics of human visual system. The 3D measurement using monocular vision system must be achieved a camera calibration. So far, the camera calibration technique required reference target in a scene. But, these methods are inefficient because they have many calculation procedures and difficulties in analysis. Therefore, this paper proposes a native method that without reference target in a scene. We use the grid type frame with different line widths. This method uses vanishing point concept that possess a rotation parameter of the camera and perspective ration that perfect each line widths into a image. We confirmed accuracy of calibration parameter estimation through experiment on the algorithm with a grid paper with different line widths.