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研究生： 丁浩展
Hao-Chan Ting
論文名稱： 基於RGB-D影像之人體骨架修正技術
Human Skeleton Correction Based on RGB-D Image
指導教授： 阮聖彰
Shanq-Jang Ruan
口試委員: 陳維美
Wei-Mei Chen
吳晉賢
Chin-Hsien Wu
林昌鴻
Chang Hong Lin
林淵翔
Yuan-Hsiang Lin
學位類別： 碩士
Master
系所名稱： 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年： 2013
畢業學年度： 102
語文別： 英文
論文頁數： 63
中文關鍵詞： 體感操作 、深度圖 、人體骨架 、物體偵測
外文關鍵詞： Motion Sensing, Depth Image, Human Skeleton, Object Detection
相關次數： 點閱：264  下載：18 
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    • 目前取得人體骨架的技術依賴在OpenNI框架和NITE的中間套件。使用此技術，一旦人體的位置被辨識，人體骨架可以被即時的追蹤。然而，當人體手上持有物件或目標受到相對深度值影像所造成的影響，會發生不正確的骨架辨識。在此論文，我們提出一種方法可以減少這類問題，甚至是提高骨架精確度。我們偵測人手上的物件，並且利用相對深度圖過濾掉此物件，之後，人體骨架辨識可以透過NITE的中間套件取得正確的人體資訊。同時，透過此過濾方法可以輸出正確的人體資訊包含15個節點、方向及相對的信任。實驗結果顯示，當人手持有物件，透過此過濾方法可以減少影響，並且開發者可以持續保持即時的人體追蹤。

    The currently accepted human skeleton extraction techniques depend on OpenNI framework and NITE middleware. By using this technique, the human skeleton can be tracked with a real time process while human position was recognized at the beginning. However, the incorrect skeleton detection may happen when human holds an object and corresponding depth image is affected by this object. In this thesis, we propose a method to reduce this kind of problem and increase the human skeleton detection accuracy. We detect the object when human holds an object and then filter the object from corresponding depth map. After filtering the object in depth map, the human skeleton detection technique of NITE middleware will get the correct skeleton information. Meanwhile, we can obtain the human skeleton information include 15 joints positions, orientations and corresponding confidents. Experimental results show that human skeleton obtained from the proposed method can reduce the effect when human holds an object, and the process of tracking skeleton is still real time for developer.

    Table of Contents Recommendation Form Committee Form Chinese Abstract English Abstract Acknowledgements Table of Contents List of Tables List of Figures 1 Introduction 1.1 Introduction to Motion Sensing 1.2 Motivation 1.3 Organization of This Thesis 2 Related Works 2.1 Background Subtraction 2.2 Σ-Δ Estimation 2.3 Skin Detection 3 Research Platform 3.1 Architecture of Xtion Pro Live 3.2 Xtion Software Tools 3.3 Capabilities of The OpenNI Tools 3.4 Human Skeleton Analysis 4 Proposed method 4.1 The Architecture of The Proposed Method 4.2 Correction of depth generator 4.3 Refinement of hand generator 4.4 Skin detection 4.5 Boundary check 5 Experimental Results 5.1 Qualitative measurement 5.2 Quantitative metrics 5.3 Quantitative measurement 6 Conclusions References Copyright Form

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