• 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.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.7
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• pp.83-91
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• 2004

The determination of camera position and orientation from known correspondences of 3D reference points and their images is known as pose estimation in computer vision or space resection in photogrammetry. This paper discusses estimation of transform parameters using the pattern matching method with 2D images only. In general, the 3D reference points or lines are needed to find out the 3D transform parameters, but this method is applied without the 3D reference points or lines. It uses only two images to find out the transform parameters between two image. The algorithm is simulated using Visual C＋＋ on Windows 98.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.367-371
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• 2015

In this paper, a novel localization method for a monocular camera is proposed by using a feature-based probabilistic map. The localization of a camera is generally estimated from 3D-to-2D correspondences between a 3D map and an image plane through the PnP algorithm. In the computer vision communities, an accurate 3D map is generated by optimization using a large number of image dataset for camera pose estimation. In robotics communities, a camera pose is estimated by probabilistic approaches with lack of feature. Thus, it needs an extra system because the camera system cannot estimate a full state of the robot pose. Therefore, we propose an accurate localization method for a monocular camera using a probabilistic approach in the case of an insufficient image dataset without any extra system. In our system, features from a probabilistic map are projected into an image plane using linear approximation. By minimizing Mahalanobis distance between the projected features from the probabilistic map and extracted features from a query image, the accurate pose of the monocular camera is estimated from an initial pose obtained by the PnP algorithm. The proposed algorithm is demonstrated through simulations in a 3D space.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07b
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• pp.1227-1230
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• 2003

In this paper, we propose a pose estimation method using local map acquired from 2d laser range finder information. The proposed method uses extended kalman filter. The state equation is a navigation system equation of Nomad Super Scout II. The measurement equation is a map-based measurement equation using a SICK PLS 101-112 sensor. We describe a map consisting of geometric features such as plane, edge and corner. For pose estimation we scan external environments by laser rage finer. And then these data are fed to kalman filter to estimate robot pose and position. The proposed method enables very fast simultaneous map building and pose estimation.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.191-197
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• 2022

This study proposes an image-based Pose Intention Network (PIN) algorithm for rehabilitation via patients' intentions. The purpose of the PIN algorithm is for enabling an active rehabilitation exercise, which is implemented by estimating the patient's motion and classifying the intention. Existing rehabilitation involves the inconvenience of attaching a sensor directly to the patient's skin. In addition, the rehabilitation device moves the patient, which is a passive rehabilitation method. Our algorithm consists of two steps. First, we estimate the user's joint position through the OpenPose algorithm, which is efficient in estimating 2D human pose in an image. Second, an intention classifier is constructed for classifying the motions into three categories, and a sequence of images including joint information is used as input. The intention network also learns correlations between joints and changes in joints over a short period of time, which can be easily used to determine the intention of the motion. To implement the proposed algorithm and conduct real-world experiments, we collected our own dataset, which is composed of videos of three classes. The network is trained using short segment clips of the video. Experimental results demonstrate that the proposed algorithm is effective for classifying intentions based on a short video clip.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.156-157
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• 2022

본 논문에서는 선택적 시점에서의 2D 포즈 추정(pose estimation) 결과를 정합 하여 정확도 높은 3D 스켈레톤(skeleton)을 만들어 낸다. 여러 프레임의 3D 데이터를 10 도 간격으로 36 방향에서 투영한 뒤, 2D 포즈 추정 결과 신뢰도가 높은 시점에서의 결과만을 선별하여 3 차원으로 정합 한다. 이때 사용하는 시점의 개수를 달리하며 정확도에 미치는 영향을 분석하여 실험적으로 정확도가 높은 최소의 시점 개수를 정하였다. 또한, 정합 한 3D 뼈대를 모션 캡쳐(motion capture) 센서와 비교하여 제안하는 알고리즘에 의해 3D 포즈 추정의 정확도가 향상되는 것을 확인했다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2579-2585
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• 2010

This paper proposes a dynamic human pose tracking method using motion-based search strategy from an image sequence obtained from a monocular camera. The proposed method compares the image features between 3D human model projections and real input images. The method repeats the process until predefined criteria and then estimates 3D human pose that generates the best match. When searching for the best matching configuration with respect to the input image, the search region is determined from the estimated 2D image motion and then search is performed randomly for the body configuration conducted within that search region. As the 2D image motion is highly constrained, this significantly reduces the dimensionality of the feasible space. This strategy have two advantages: the motion estimation leads to an efficient allocation of the search space, and the pose estimation method is adaptive to various kinds of motion.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.24-34
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• 2007

Application systems that easy to access a information have been developed by IT growth and a human life variation. In this paper, we propose a application system to register a 3D footwear model using a monocular camera. In General, a human motion analysis research to body movement. However, this system research a new method to use a foot movement. This paper present a system process and show experiment results. For projection to 2D foot plane from 3D shoe model data, we construct processes that a foot tracking, a projection expression and pose estimation process. This system divide from a 2D image analysis and a 3D pose estimation. First, for a foot tracking, we propose a method that find fixing point by a foot characteristic, and propose a geometric expression to relate 2D coordinate and 3D coordinate to use a monocular camera without a camera calibration. We make a application system, and measure distance error. Then, we confirmed a registration very well.

• Journal of the Korea Computer Graphics Society
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• v.24 no.3
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• pp.61-71
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• 2018

When triangular meshes are generated from the point clouds in global space reconstructed through camera pose estimation against captured RGB-D streams, the quality of the resulting meshes improves as more triangles are hired. However, for 3D reconstructed models beyond some size threshold, they become to suffer from the ugly-looking artefacts due to the insufficient precision of RGB-D sensors as well as significant burdens in memory requirement and rendering cost. In this paper, for the generation of 3D models appropriate for real-time applications, we propose an effective technique that extracts high-quality textures for moderate-sized meshes from the captured colors associated with the reconstructed point sets. In particular, we show that via a simple method based on the mapping between the 3D global space resulting from the camera pose estimation and the 2D texture space, textures can be generated effectively for the 3D models reconstructed from captured RGB-D image streams.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.735-739
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• 2005

This paper presents a new system to estimate the head pose of human in interactive indoor environment that has dynamic illumination change and large working space. The main idea of this system is to suggest a new morphological feature for estimating head angle from stereo disparity map. When a disparity map is obtained from stereo camera, the matching confidence value can be derived by measurements of correlation of the stereo images. Applying a threshold to the confidence value, we also obtain the specific morphology of the disparity map. Therefore, we can obtain the morphological shape of disparity map. Through the analysis of this morphological property, the head pose can be estimated. It is simple and fast algorithm in comparison with other algorithm which apply facial template, 2D, 3D models and optical flow method. Our system can automatically segment and estimate head pose in a wide range of head motion without manual initialization like other optical flow system. As the result of experiments, we obtained the reliable head orientation data under the real-time performance.