人體姿態的分析於行為辨識、人機介面互動及監控系統的發展及應用占有重要地位。無標記式人體姿態分析可提供非侵入式且高自由度的姿勢擷取，但因為動作自由度高且人物衣著變化大，因此其挑戰極高。本論文提出一個新式的人體姿態擷取方法，該方法針對單張影像，定位出二維影像中人體關節點位置，並還原在三維空間中的人體姿勢。本論文方法建立有向貝式網路模型，並提出以Annealing Gibbs Sampling 推論法估算該模型節點之機率，以求得人體關節於空間中的位置。實驗使用HumanEva影像資料庫來驗證提出方法的有效性，實驗人員無任何衣著限制及標記，實驗動作為高自由性的繞圈步行運動。實驗結果證明本論文提出之方法可有效的估測影像中人體的姿態。

Human pose estimation method is important for the development of behavior recognition, human-robot interaction and visual surveillance. Markerless human pose estimation method can provides non-intrusive and high-free motion capture. It has great challenges due to large range variations of motion and clothe. We propose a novel human motion capture method. The proposed method can locate human body joint position and reconstruct the human pose in 3D space from monocular images. We propose a directed probabilistic graphical model to estimate human joint positions by a devised annealing Gibbs sampling inference method. Experiments are conducted on HumanEva dataset to show the effectiveness of the proposed method. Subjects in the HumanEva have no clothe lamination and markers. The experimental data are image sequences of walking motion around a region with large ranges variation of pose. Experimental results show that the proposed method can estimate human pose from monocular images efficiently.

Abstract(in Chinese).......................................................................................................... i
Abstract............................................................................................................................ ii
Acknowledgement(in Chinese) ....................................................................................... iii
Contents.......................................................................................................................... iv
List of Figures................................................................................................................... v
List of Tables.................................................................................................................. vii
Chapter 1 Introduction...................................................................................................... 1
1.1 Background............................................................................................................. 1
1.2 Motivation .............................................................................................................. 4
1.3 Brief introduction to probabilistic graphical models.............................................. 5
1.4 System architecture ................................................................................................ 9
1.5 The organization of this thesis.............................................................................. 10
Chapter 2 Review of Previous Work.............................................................................. 11
Chapter 3 Feature Extraction.......................................................................................... 16
3.1 Human silhouette.................................................................................................. 16
3.2 Normalized center of human body ....................................................................... 17
3.3 Spatial distribution of skin color .......................................................................... 19
3.4 Corners of lower body .......................................................................................... 21
Chapter 4 Pose Estimation Framework .......................................................................... 23
4.1 Articulated human model ..................................................................................... 24
4.2 Approximate inference ......................................................................................... 28
4.3 Component-Based Metropolis-Hastings approach............................................... 30
4.4 Component-based Gibbs sampling approach ....................................................... 32
4.5 Component-Based annealing Gibbs sampling approach...................................... 36
Chapter 5 Experimental result ........................................................................................ 38
5.1 Database ............................................................................................................... 38
5.2 2D pose estimation result ..................................................................................... 42
5.3 3D pose estimation result ..................................................................................... 51
Chapter 6 Conclusion ..................................................................................................... 61
References ...................................................................................................................... 63

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