• Journal of Broadcast Engineering
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• v.25 no.7
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• pp.1095-1106
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• 2020

In this paper, we propose an algorithm that extracts a high-precision 3D skeleton using a model generated using a distributed RGB-D camera. When information about a 3D model is extracted through a distributed RGB-D camera, if the information of the 3D model is used, a skeleton with higher precision can be obtained. In this paper, in order to improve the precision of the 2D skeleton, we find the conditions to obtain the 2D skeleton well using the PCA. Through this, high-quality 2D skeletons are obtained, and high-precision 3D skeletons are extracted by combining the information of the 2D skeletons. Even though this process goes through, the generated skeleton may have errors, so we propose an algorithm that removes these errors by using the information of the 3D model. We were able to extract very high accuracy skeletons using the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.731-736
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• 2008

In this paper, we propose the movement detection algorithm by using virtual skeleton model. To do this, first, we eliminate error values by using conventioanl method based on RGB color model and eliminate unnecessary values by using the HSI color model. Second, we construct the virtual skeleton model with skeleton information of 10 peoples. After matching this virtual model to original image, we extract the real head silhouette by using the proposed circle searching method. Third, we extract the object by using the mean-shift algorithm and this head information. Finally, we validate the applicability of the proposed method through the various experiments in a complex environments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.764-767
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• 2009

Motion capture systems allow to measure the precise position of markers on the human body in real time. These captured motion data, the marker position data, have to be fitted by a human skeleton model to represent the motion of the human. Typical human skeleton models approximate the joints using a ball joint model. However, because this model cannot represent the human skeleton precisely, errors between the motion data and the movements of the simplified human skeleton model happen. We propose in this paper a method for measuring a translation component of wrist, and elbow joints on upper limb using optical motion capture system. Then we study the errors between the ball joint model and acquired motion data. In addition, we discuss the problem to estimate motion of human joint using optical motion capture system.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.996-1008
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• 2009

This paper proposes a novel approach for the model-based pose matching of a human body using Active Shape Models. To improve the processing time of model creation and registration, we use a skeleton-type model instead of the conventional silhouette-based models. The skeleton model defines feature information that is used to match the human pose. Images used to make the model are for 600 human bodies, and the model has 17 landmarks which indicate the body junction and key features of a human pose. When applying primary Active Shape Models to the skeleton-type model in the matching process, a problem may occur in the proximal joints of the arm and leg due to the color variations on a human body and the insufficient information for the fore-rear directions of profile normals. This problem is solved by using the background subtraction information of a body region in the input image and adding a 4-directions feature of the profile normal in the proximal parts of the arm and leg. In the matching process, the maximum iteration is less than 30 times. As a result, the execution time is quite fast, and was observed to be less than 0.03 sec in an experiment.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.1030-1031
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• 2014

본 논문에서는 3D Skeleton Model로 획득된 관절 정보를 이용하여 제스처를 인식 할 수 있는 방법을 제안한다. 사람마다 각기 다른 신체 비율을 가지지만 각 관절 또는 신체의 구조는 같다는 사실을 바탕으로 관절의 각도를 기반으로 제스처를 인식하는 방법에 대해 제안한다.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.361-369
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• 2022

Unsafe actions and behaviors of workers cause most accidents at construction sites. Nowadays, occupational safety is a top priority at construction sites. However, this problem often requires money and effort from investors or construction owners. Therefore, decreasing the accidents rates of workers and saving monitoring costs for contractors is necessary at construction sites. This study proposes an unsafe behavior detection method based on a skeleton model to classify three common unsafe behaviors on the scaffold: climbing, jumping, and running. First, the OpenPose method is used to obtain the workers' key points. Second, all skeleton datasets are aggregated from the temporary size. Third, the key point dataset becomes the input of the action classification model. The method is effective, with an accuracy rate of 89.6% precision and 90.5% recall of unsafe actions correctly detected in the experiment.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1101-1104
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• 2019

키넥트는 RGBD 카메라로 인체의 뼈대와 관절을 3D 공간에서 스켈레톤 데이터수집을 가능하게 해주었다. 스켈레톤 데이터를 활용한 행동 분류는 RNN, CNN 등 다양한 인공 신경망으로 접근하고 있다. 본 연구는 키넥트를 이용해서 Skeleton Joints를 수집하고, DNN 기반 스켈레톤 모델링 학습으로 행동을 분류한다. Skeleton Joints Processing 과정은 키넥트의 Depth Map 기반의 Skeleton Tracker로 25가지 Skeleton Joints 좌표를 얻고, 학습을 위한 전처리 과정으로 각 좌표를 상대좌표로 변경하고 데이터 수를 제한하며, Joint가 트래킹 되지 않은 부분에 대한 예외 처리를 수행한다. 스켈레톤 모델링 학습 과정에선 3계층의 DNN 신경망을 구축하고, softmax_cross_entropy 함수로 Skeleton Joints를 집는 모션, 내려놓는 모션, 팔짱 낀 모션, 얼굴을 가까이 가져가는 모션 해서 4가지 행동으로 분류한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1677-1678
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• 2015

본 논문에서는 3D Skeleton Model로부터 획득된 관절 정보를 이용하여 제스처를 인식할 수 있는 방법을 제안한다. 사람의 신체 크기나 비율은 다르더라도 구조는 같다는 사실을 바탕으로, 관절과 관절이 이루는 각도를 이용해 제스처를 인식한다. 몇 가지 제스처를 선정한 뒤, 실험을 통해 제안한 방법의 인식률을 측정해 보았다. 또한 동적 제스처 인식을 위한 기초를 다지기 위해 이동 방향과 이동 거리, 이동 위치를 측정하는 실험을 해 보았다.

• Journal of Korea Game Society
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• v.8 no.2
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• pp.47-55
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• 2008

This paper introduces that SSFE(Skeleton System for Facial Expression) to deform facial expressions by rigging of skeletons does same functions with 14 facial muscles based on anatomy. A three dimensional animation tool (MAYA 8.5) is utilized for making the SSFE that presents deformation of mesh models implementing facial expressions around eyes, nose and mouse. The SSFE has a good reusability within diverse human mesh models. The reusability of SSFE can be understood as OSMU(One Source Multi Use) of three dimensional animation production method. It can be a good alternative technique for reducing production budget of animations. It can also be used for three dimensional animation industries such as virtual reality and game.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1062-1067
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• 2005

This paper discusses a skeleton feature extraction method for ubiquitous robot system. The skeleton features are used to analyze human motion and pose estimation. In different conventional feature extraction environment, the ubiquitous robot system requires more robust feature extraction method because it has internal vibration and low image quality. The new hybrid silhouette extraction method and adaptive skeleton model are proposed to overcome this constrained environment. The skin color is used to extract more sophisticated feature points. Finally, the experimental results show the superiority of the proposed method.