• IEIE Transactions on Smart Processing and Computing
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• 제4권4호
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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.

• 로봇학회논문지
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• 제12권3호
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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.

• 한국정밀공학회지
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• 제12권8호
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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.

• 제어로봇시스템학회논문지
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• 제22권6호
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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.

• 방송공학회논문지
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• 제19권6호
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• pp.942-956
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• 2014

본 논문은 단안의 RGB 카메라를 이용하는 실시간 손 추적 방법을 제안한다. 손은 높은 degrees of freedom을 가지고 있기 때문에 손 추적은 높은 모호성을 가지고 있다. 따라서 제안하는 방법에서는 손 추적의 모호성을 줄이기 위해서 단계별 손 추적 전략을 채택하였다. 제안하는 방법의 추적 과정은 손바닥 포즈 추적, 손가락 yaw 움직임 추적, 그리고 손가락 pitch 움직임 추적, 세 단계로 구성되어 있으며, 각 단계는 순서대로 수행된다. 제안하는 방법은 손은 평면으로 간주할 수 있다고 가정하고, 평면 손 모델을 이용한다. 평면 손 모델은 손 모델을 현재의 사용자 손 모양에 맞춰서 변경하는 손 모델 재생성을 가능하게 하는데, 이는 제안하는 방법의 강건성과 정확도를 증가시킨다. 그리고 제안하는 방법은 실시간 연산이 가능하고 GPU 기반 연산을 요구하지 않기 때문에, Google Glass와 같은 모바일 장비를 포함한 다양한 환경에 적용가능하다. 본 논문은 다양한 실험을 통해서 제안하는 방법의 성능과 효용성을 입증한다.

• 한국항공우주학회지
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• 제37권3호
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• pp.267-274
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• 2009

최근 무인 항공기(Unmanned Aerial Vehicle, UAV)는 다양한 임무수행이 가능한 무인 시스템이라는 점에서 크게 주목받고 있다. 특히 정찰, 추적 등의 임무는 영상을 이용하여 임무 수행이 이루어진다. 소형 무인 항공기의 경우 중량과 비용을 고려하여 단안 영상을 이용하는 임무 수행 연구가 활발하게 이루어지고 있다. 그러나 실제 지표면과 목표물이 고도 차이를 가지고 있어, 영상의 상대깊이를 고려하지 않은 3차원 거리는 임무 수행 시 오차 요인으로 작용 할 수 있다. 본 연구에서는 상대 깊이 추정을 위한 평균이동 알고리즘, 광류, 부분 공간법에 관하여 차례로 제시한다. 평균이동 알고리즘은 영상 내 목표물 추적과 관심영역을 결정하며 광류는 영상의 자기를 이용한 영상 이동 정보를 포함한다. 마지막으로 부분 공간법은 영상안의 움직임을 추정하며 각 영역의 상대깊이를 결정한다.

• 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.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 춘계학술발표대회
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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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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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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.