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研究生：	林守仁 Lin, Shou-Jen
論文名稱：	使用深度和骨架資訊在動作辨識上類內變化問題之研究 The Study on Using Depth Image and Skeleton in Intra-class Variation Problem for Human Action Recognition
指導教授：	楊竹星 Yang, Chu-Sing
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 電腦與通信工程研究所 Institute of Computer & Communication Engineering
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	106
中文關鍵詞：	深度影像、方向梯度值方圖、流形學習、運動歷史圖、動態時間扭曲法、動作辨識
外文關鍵詞：	Depth Image, Histogram of Oriented Gradient, Manifold Learning, Motion History Image, Dynamic Time Warping, Action recognition
相關次數：	點閱：105 下載：6
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不同的辨識方法論著手於識別和了解人類動作的變化，動作辨識中的類別內變化，像是同一動作不同情境、自身遮蔽、視角的改變、低解析度、複雜背景等，都是由不確定因素造成。然而，某些類別內變化仍是一個需要去解決的困難工作。例如，相同動作因快慢有不同的運動時間、自我遮蔽與不同視角等都是動作辨識議題上主要挑戰。過去的動作辨識方法中有些研究忽略這些問題，有些採用比較複雜的方式處裡這些問題。在本篇論文中，我們集中在解決因運動快慢而有不同運動時間和運動自我遮蔽的問題，這些是由運動時自然因素和運動中由於同一區域動作重疊而造成，我們提出三個以深度資訊為基礎的方法來應付這個問題。第一個方法，使用深度資訊獲取目標物，並用三維連通方法將目標物連接完整，接著得到目標件的梯度，並用方向梯度值方圖來做為影像特徵，最後降維投影並與資料庫比較識別。第二個方法，使用外表為基礎的樣板匹配法，提供比較簡單和快速的動作分析。目標物資料包含深度和二維資料被投影到三個正交平面。沿著光軸的深度運動能在這些正交平面清楚描述並作為動作特徵，以運動能量和運動角度變化為基礎的時間切割方法，將複雜的運動切割成幾個簡單運動。三維資料被進一步用到去得到運動歷史軌跡，並由運動歷史影像圖來描述運動的三個面向，接著相對應的權重去強化主要的運動面向。就特徵萃取方面，使用多面向歷史直方圖能有效降低計算負擔並達成較高的辨識率。第三個方法，使用深度影像和骨架關節資訊，每個關節座標轉化為關節方向，這個關節方向變化的時間序列，不受身體的大小的影響，接著使用動態時間扭曲法到特徵向量的比對，期間為了增加動態時間扭曲法的運算效率，引入累積旋轉變化量去預分類和改善動態時間扭曲法的權重，經由試驗顯示，提出的方法能提升計算速率和提高辨識率。

Various recognition methodologies address to recognize and understand varieties of human actions. Intra-class variation in action recognition, such as different contexts of the same actions, occlusions, viewpoints changes, low resolution and cluttered background, is caused by various uncertainty factors. However, some of intra-class variation is a daunting task to solve. For example, different motion duration of the same action, self-occlusion, different viewpoints are the major challenges in action recognition. Various action recognition methods either bypass this problem or solve this problem in complex manner.

In this dissertation, we concentrate on different motion duration of the same action and motion self-occlusion problem, These are cause by natural factors of human movement and motion overlapping in various complex action. We proposed three methods based on depth image to address these challenges.
In first method, the target object is acquired by using 3-dimensional connected component labelling. The gradient is then obtained from the depth object, creating a histogram of oriented gradient (HOG) as the feature for human action. Finally, continuous motions are matched with their corresponding low-dimensional projections using the manifold learning method.
In second method, we used appearance-based template matching paradigms which are simpler and faster for action analysis. The target data, depth and 2D data, are projected to three orthogonal planes. The action featured in the depth motion along the optical axis can clearly describe its trajectory by these orthogonal planes. Based on the change of motion energy and the angle variations of motion orientations, temporal segmentation method automatically segments the complex action into several simple movements. 3D Data is further applied to acquire the three viewpoints' motion history trajectory, whereby the motion of a target is described through the motion history images (MHIs) from the three viewpoints. The weightings corresponding to the gradients of the MHIs are included for determining the viewpoint that can best describe the motion of the target. In terms of feature extraction, the application of multi-resolution motion history histograms can effectively reduce the computational load and achieve a high recognition rate. Experimental results show that the proposed method can effectively solve the self-occlusion problems.
In third method, we used the depth image and capture the joints from human skeleton structure. Each joint is converted 3D coordinate into joint orientation. After that, we build our feature vector from joint orientation along time series that invariant to human body size instead of 3D coordinate of body skeleton joints. Dynamic Time Warping is then applied to the resulted feature vector. However, the drawback of Dynamic Time Warping method is ineffective to computing result. Therefore, we use accumulative angular variation of skeleton joints to analysis action, and then divide actions into several groups. The proposed method can improve the calculated speed and recognition rate.

Approval Letter in Chinese . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Approval Letter in English . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Abstract in Chinese . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract in English . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Overview of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . 1
2.1 Using Manifold Learning and Gradient Feature of Depth Image . . 2
2.2 Using Motion History Image Based Temporal Segmentation . . . . 3
2.3 Using Weighted 3D Skeleton Joints . . . . . . . . . . . . . . . . . 4
3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
4 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Background Knowledge and Related Work . . . . . . . . . . . . . . . . . . . . . 6
1 Depth Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Manifold Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1 Locality Preserving Projections . . . . . . . . . . . . . . . . . . . 11
3 Histogram of Oriented Gradient . . . . . . . . . . . . . . . . . . . . . . . 13
4 Motion History Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5 Dynamic Time Warping . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6 Rotational Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.1 Euler Angles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.2 Quaternions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Using Manifold Learning and Gradient Feature of Depth Image . . . . . . . . . . 22
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2 System Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 3D Connected Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5 Gradient Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . 28
6 Training and Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
7 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
7.1 A Comparison of Background Filter Methods . . . . . . . . . . . . 33
7.2 Manifold Mapping Result . . . . . . . . . . . . . . . . . . . . . . 33
7.3 Recognition Results . . . . . . . . . . . . . . . . . . . . . . . . . 37
7.4 Improvement on Matching Kernel . . . . . . . . . . . . . . . . . . 38
8 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Using Motion History Image Based Temporal Segmentation . . . . . . . . . . . . 41
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2 System Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3 Virtual volumetric silhouette generation . . . . . . . . . . . . . . . . . . . 43
4 Temporal Segmentation Motion History Image (TS-MHI) . . . . . . . . . 44
4.1 Temporal Segmentation . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 Motion Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3 Motion Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.4 Motion Region and Static Region . . . . . . . . . . . . . . . . . . 47
4.5 Algorithm for Temporal Segmentation . . . . . . . . . . . . . . . . 50
4.6 Projecting Three-Viewpoint Motion History Image . . . . . . . . . 50
4.7 Temporal and Spatial normalization . . . . . . . . . . . . . . . . . 53
4.8 Weighted Motion History Image . . . . . . . . . . . . . . . . . . . 54
5 Multi-Resolution Motion History Histogram . . . . . . . . . . . . . . . . 55
6 Comparison and Classification . . . . . . . . . . . . . . . . . . . . . . . . 58
7 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
7.1 Comparative Analysis of Recognition Results . . . . . . . . . . . 61
7.2 Discussion of The Sequence with Multi-Action . . . . . . . . . . . 66
8 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
Using Weighted 3D Skeleton Joints . . . . . . . . . . . . . . . . . . . . . . . . . 71
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
2 System Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3 Body Parts Representation . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.1 Kinect Joints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.2 Bone Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.3 Joint Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.4 Action Coordinates Description for Skeletal Joints . . . . . . . . . 75
4 Features Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5 The Accumulative Amount of Rotation Variation . . . . . . . . . . . . . . 78
5.1 The Analysis of ARV in Each Joint . . . . . . . . . . . . . . . . . 79
6 Grouping of Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7 Weighted Dynamic Time Warping . . . . . . . . . . . . . . . . . . . . . . 82
7.1 Warping function restrictions . . . . . . . . . . . . . . . . . . . . . 82
8 Distance metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
8.1 1-Nearest Neighbour Classification . . . . . . . . . . . . . . . . . 85
9 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
9.1 Comparative Analysis of Recognition Results . . . . . . . . . . . 86
9.2 The Recognition Rate of Joints Number . . . . . . . . . . . . . . . 92
9.3 The Distance of Intra-class and Inter-class . . . . . . . . . . . . . . 93
10 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Biography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

                                    

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校內：2021-07-01公開
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