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Author: 許嘉豐
Richard Sugiarto
Thesis Title: 使用具有動態時間扭曲的多個攝像機的3D重建姿勢進行運動學習
Motion Learning using 3D Reconstruction Pose from Multiple Cameras with Dynamic Time Warping
Advisor: 楊傳凱
Chuan-Kai Yang
Committee: 林伯慎
Bor-Shen Lin
賴源正
Yuan-Cheng Lai
Degree: 碩士
Master
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2022
Graduation Academic Year: 110
Language: 英文
Pages: 53
Keywords (in Chinese): Human PoseOpenPoseIntrinsic Camera CalibrationExtrinsic Camera CalibrationDynamic Time Warping
Keywords (in other languages): Human Pose, OpenPose, Intrinsic Camera Calibration, Extrinsic Camera Calibration, Dynamic Time Warping
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  • Motion Learning is a common task nowadays. Motion Learning can be done by comparing all the joints of a skeleton with those of other skeletons. A pose estimator is used to get each joint’s location. There are many pose estimators such as OpenPose, DensePose, and many more. In this thesis, OpenPose is used as the 2D pose estimator. In general, a 2D pose estimator is not enough to cover all the pose information because the 2D pose estimator only focuses from one viewing direction. To overcome this problem, a multiple-camera approach is used to get more information from multiple views. Camera calibration is needed for each camera because multiple cameras are used. From the calibration, a 3D coordinate can be obtained. Finally, scoring can be done since the pose estimator produces 3D pose information. Dynamic Time Warping (DTW) is used for the scoring calculation. DTW is an algorithm that can measure the similarity between two temporal sequences. DTW also can handle the problem if the two temporal sequences have different speeds or different number of frames.

    Abstract I Acknowledgment II Table of Contents III List of Figures V List of Tables VI Chapter 1. Introduction 1 1.1 Background 1 1.2 Contribution 2 1.3 Research Outline 2 Chapter 2. Related Works 3 2.1 Human Pose Estimation 3 2.2 Camera and Calibration 5 2.3 Pose Comparison 5 2.4 Kalman Filter 6 Chapter 3. Proposed System 8 3.1 System Architecture 8 3.2 2D Pose Estimation 9 3.3 Camera Projection Matrix and the calibration 10 3.4 Noise Reduction 15 3.5 Scoring Phase 17 Chapter 4. Experimental Results 20 4.1 Experiments Parameter 20 4.2 Experimental Results 21 Chapter 5. Conclusion and Discussion 41 5.1 Conclusion 41 References 42

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