臺灣博碩士論文加值系統

近年來，隨著低成本的深度攝影機問世，藉此直接擷取到包含人體關節座標的資料稱之為體感資料，而分析高維度之體感資料時往往需耗費大量的計算時間，使得這個問題具有相當的挑戰性；在許多替代方案中，類神經網路當中的自組織映射圖被證實在處理體感資料上有不錯的效果，而其特色在於透過訓練後的自組織映射圖，可將一串體感資料序列容易且精確的對應成一串動作索引；但是，自組織映射圖的訓練過程中相當耗時且過程繁瑣。有鑑於此，本研究採用了結合階層式架構至自組織映射圖的方法當中；本研究的方法又稱作生長四元樹自組織映射圖，目的在於幫助我們有效減少訓練時的計算複雜度，並且同時保留了自組織映射圖技術的特色。最後，我們採用了一個WorkoutSU-10 的測試資料集來進行實驗評估，結果顯示我們所提出的方法對於檢索出相似的動作序列有不錯的效率和顯著的檢索效果。

With low-cost depth cameras are released recently, motion data containing 3D coordinates of skeleton joints during a time period can be directly captured. Nevertheless, analyzing the motion data is usually a challenging problem and requires huge computation costs because of the high-dimensionality. Among several alternatives, the self-organizing map (SOM) is verified to be an effective technique to handle such motion data. Specifically, a captured motion sequence can be easily and precisely mapped to form an indexed motion string through the use of a trained SOM. However, the training process of the SOM is high computation complexity and is thus typically tedious. In view of this, we propose in this work to incorporate a hierarchical structure into the SOM technique. Generally speaking, our approach named as GQSOM (growing quadtree self-organizing map) helps to significantly reduce the required computation complexity while preserving the effectiveness of the SOM technique. Empirical studies using the WorkoutSU-10 exercise dataset show that our approach is both efficient and effective to perform indexing and retrieval tasks of motion data.

Chapter 1 Introduction 1
1.1 Motivation and Overview of the Thesis 1
1.2 Contributions of the Thesis 2
Chapter 2 Literature Survey 3
2.1 Human Action Analysis from Motion Data 3
2.2 Preprocessing of Motion Data 5
2.3 Indexing and Retrieval of Motion Data 7
2.3.1 Common Approach for Indexing and Retrieval 7
2.3.2 Challenges of Indexing Human Motion Sequences 12
Chapter 3 Using a Hierarchical Structure for Indexing and Retrieval 13
3.1 Indexing Human Action Sequence based on a Hierarchal Structure 13
3.2 Retrieval and Similarity Comparison 18
3.3 Proposed Scheme 21
Chapter 4 Empirical Studies 22
4.1 Experimental Environment 22
4.2 Experimental Process 23
4.3 Experimental Results 24
Chapter 5 Conclusions and Future Works 32
Bibliography 33

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