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研究生： 陳仕昇
Shih-Sheng Chen
論文名稱： 基於人體骨架辨識之動作辨識系統
A Moving Action Recognition System Based on Human Skeletonization
指導教授： 阮聖彰
Shanq-Jang Ruan
口試委員: 林昌鴻
Chang-Hong Lin
姚智原
Chih-Yuan Yao
蔡坤霖
Kun-Lin Tsai
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： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 65
中文關鍵詞： 體感 、深度攝影機 、動作辨識 、支持向量機
外文關鍵詞： motion-sensing, dpeth camera, action recognition, support vector machine(SVM)
相關次數： 點閱：300  下載：2 
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    • 近年來，體感技術的發展愈來愈迅速，應用也愈來愈全面。不需傳統的控制器，人們可以在此技術獲得很多方便及做更多有趣的應用。在開發體感應用時，由於人的身形、骨架、結構皆有著全面性的不同，每一種動作控制都是繁雜的設計。在這篇論文，我們提出一種使用骨架資訊的動作辨識系統，透過深度攝影機結取人體骨架資訊，經由座標轉換後，針對所要辨識的十四種動作的區域空間做分類收集特徵序列，最後再利用支持向量機(Support Vector Machine)去對特徵序列做分類。實驗結果顯示所提出的系統可以正確的偵測十四種動作。

    Nowadays, the applications of motion-sensing technology have been growing up quickly. With motion-sensing technology, human can do more convenience and interesting applications without traditional controller. In developing motion-sensing applications, a multifarious design for every kinds of action control. Besides, it is difficult to design an overall action recognition method due to the different skeleton structure between each human. In this thesis, an action recognition system is provided by using information of human skeletons obtained from RGB-D camera. The region space is classified and collected feature sequence for fourteen actions via the coordinate transformation. Finally, the support vector machine is used to classify the feature sequence. The experimental result shows that the proposed system can recognize fourteen actions correctly.

    Recommendation Form Committee Form Chinese Abstract English Abstract Acknowledgements Table of Contents List of Tables List of Figures 1 Introduction 1.1 Introduction of human motion technology 1.2 Introduction of motion detector progression 1.3 Motivation of motion detector 1.4 Organization of this thesis 2 Related Works 2.1 A Review of Human Activity Analysis 2.1.1 Space-Time Approaches 2.1.2 Sequential Approaches 2.2 Support Vector Machine 3 Proposed Method 3.1 The Architecture of Proposed Method 3.1.1 The Data Modeling flow 3.1.2 The Posture recognition flow 3.1.3 Section Summary 3.2 Coordinate transform scheme 3.2.1 Coordinate transformation technique 3.3 Region Space Model 3.4 Extraction of Action Feature 4 Experimental Results 4.1 Developing platform 4.2 Analysis of region space and action feature 4.3 Experimental results of proposed method 5 Conclusion References Copyright Form

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