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研究生:沈保成
研究生(外文):Bau-Cheng Shen
論文名稱:在兩正交視野下以模型為基礎的即時人體運動參數分析系統
論文名稱(外文):A Real-Time Model-Based Human Motion Analysis System from Two Orthogonal Views
指導教授:黃仲陵黃仲陵引用關係
指導教授(外文):Chung-Lin Huang
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:67
中文關鍵詞:正交視野以模型為基礎人體運動分析系統即時系統人體運動追蹤系統投影分析濾波器虛擬人物
外文關鍵詞:orthogonal viewsmodel-basedhuman motion analysis systemreal-time systemhuman motion tracking systemKalman filtermodel-based matchingprojection profile
相關次數:
  • 被引用被引用:1
  • 點閱點閱:157
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  • 下載下載:8
  • 收藏至我的研究室書目清單書目收藏:0
在本篇論文中,我們發展了一個在兩正交視野下以模型為基礎的即時人體運動參數分析系統。我們使用兩台攝影機去抓取正面和側面的人體運動資訊,為了追蹤人體的動作,我們對雙手和雙腿使用了兩種不同的方法去估算人體運動時的關節角度,首先,我們使用Kalman Filter去預測並修正雙手的姿勢,然後再透過比較影像中前景物體與二維人體模型的相似程度,調整出最佳的人體腿部關節角度,然後再整合雙手和雙腿的關節角度來得到一個完整的動作。我們的人體運動分析系統分成巨觀運動分析和微觀運動分析,巨觀運動分析經由分析二元影像的垂直投影和側投影得到雙手和雙腿動作的基本資訊,然後再使用Kalman Filter做微觀運動分析去追蹤雙手動作的關節角度,以及用模型比對的方式去找到雙腿動作的關節角度。
In this thesis, we introduce a real time two orthogonal views human motion analysis system. We use two cameras to capture the facade and flank views of the human motion. To track the motion of the human object, we propose two methods to estimate the motion parameters (BAPs) of the human arm and leg. First, we use Kalman filter to predict arms posture. Second, we track the leg by model-based matching method. The human motion analysis is divided into macro motion analysis and micro motion analysis. The former identifies the certain well-defined postures and the latter traces the variation of joint angle parameters. Before tracking, we analyze the vertical projection profile and horizontal projection profile in each view to identify postures. With the identified postures, we can apply the Kalman filter to track the motion of joint angels and generate the BAPs.
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 RELATED WORK 1
1.3 SYSTEM ARCHITECTURE 4
CHAPTER 2 PREPROCESSING AND SYSTEM INITIALIZATION 8
2.1 CAMERA CALIBRATION 8
2.2 FOREGROUND OBJECT EXTRACTION 9
2.3 HUMAN MODEL 11
2.3.1 Parameters of Human Model 11
2.3.2 Homogeneous Coordinate System 13
2.4 INITIALIZATION AND BDP DETERMINATION 15
2.4.1 BDPs Estimation of Front Viewer 16
2.4.2 BDPs Estimation of Side Viewer 19
2.4.3 BDPs Integration 19
CHAPTER 3 MACRO MOTION ANALYSIS 21
3.1 FACADE/FLANK DETERMINATION 24
3.2 HUMAN POSITION ESTIMATION 25
3.3 BENDING DETERMINATION 25
3.4 ARM MOVEMENT ANALYSIS 27
3.5 ARM-OVERHEAD-RAISING ANALYSIS 35
3.6 LEG MOVEMENT ANALYSIS 36
3.6.1 Projection Profile Analysis 36
3.6.2 Model-Based Matching 38
3.7 BAP INTEGRATION 40
CHAPTER 4 MICRO MOTION ANALYSIS USING KLMAN FILTERS 42
4.1 INTRODUCTION 42
4.2 APPLICATION 46
4.3 THE TRAINING PHASE 48
CHAPTER 5 EXPERIMENTAL RESULTS 50
5.1 EXPERIMENTAL RESULTS 50
5.2 DRAWBACKS AND LIMITATIONS IN 3-D HUMAN MODEL 61
CHAPTER 6 CONCLUSIONS AND FUTURE WORKS 63
REFERENCES 64
APPENDIX 66
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[2] C. Y. Chuang and C. L. Huang, “A real time model-based human motion analysis system in multiple-view,” Master thesis, Department of Electrical Engineering in NTHU, Taiwan, 2001.
[3] T. H. Tzeng and C. L. Huang, “A model-based human motion analysis system in multiple-view,” Master thesis, Department of Electrical Engineering in NTHU, Taiwan, 2003.
[4] Osama Masoud, Nikos Papanikolopoulos, “A method for human action recognition,” Image and Vision Computing 21 (2003), 729-743
[5] S. L. Dockstader and A. M. Tekalp, “On the tracking of articulated and occluded video object motion,” Real-Time Imaging 7, 415-432 (2001).
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[7] S. L. Dockstader, M. J. Berg and A. M. Tekalp, “Stochastic kinematic modeling and feature extraction for gait analysis,” IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 12, no. 8, August 2003.
[8] R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transaction of the ASME—Journal of Basic Engineering, pp.35-45, March. 1960.
[9] T. J. Broida, S. Chandrashekhar, and R. Chellappa, “Recursive 3-D motion estimation from a monocular image sequence,” IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, vol. 26, no. 4, July. 1990.
[10] Ali Azarbayejani, Bradley Horowitz, and Alex Pentland, Perceptual Computing Section, MIT Media Laboratory, 1993.
[11] Simon Haykin, “Kalman filtering and neural networks,” John Wiley&Sons, INC., New York, 2001
[12] Berthold K. P. Horn, “Closed-form solution of absolute orientation using unit quaternions,” Reprinted from Journal of the Optical Society of America A, vol. 4, page 629, April 1987
[13] N. Krahnstover, M. Yeasin, and R. Sharma, “Toward a unified framework for tracking and analysis of human motion,” IEEE Workshop on Detection and Recognition Events in Video, 2001
[14] Thanart Horprasert, David Harwood, and Larry S. Davis, “A robust background subtraction and shadow detection,” Proceedings of the Fourth Asian Conference on Computer Vision, page 983-988, 2000
[15] I. C. Chang and C. L. Huang, “The model-based human body motion analysis system,” Image and Vision Computing, vol.18, pp.1067-1083, 2000.
[16] Greg Welch and Gary Bishop, “An introduction to the Kalman filter,” Department of Computer Science University of North Carolina at Chapel Hill Chapel Hill, NC 27599-3175, Friday, May 23, 2003
[17] Mobinder S.Grewal and Angus P.Andrews, “Kalman Filtering Theory and Practice Using MATLAB,” John Wiley&Sons, INC.
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