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研究生: 劉玠泓
Liu, Chieh-Hung
論文名稱: 三維空間之即時手部追縱與吉他和弦手勢辨識
Real-Time 3D Hand Tracking and Guitar Gesture Recognition
指導教授: 施國琛
Timothy K. Shih
口試委員:
學位類別: 碩士
Master
系所名稱: 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
論文出版年: 2016
畢業學年度: 104
語文別: 英文
論文頁數: 74
中文關鍵詞: 手指追縱吉他和弦手勢辨識點雲深度資訊即時區域搜尋
外文關鍵詞: Finger Tracking, Guitar Chord Gesture Recognition, Point Clouds, Depth Data, Real-Time, Region Search
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  • 人機互動漸漸成為電腦科學中的一個重要議題,它讓我們能更輕易地與電腦或各種設備進行交流。本篇提出了一個基於區域搜索的手部追蹤方法,我們採用了三維點雲 (Point Cloud) 資訊的方法來改善手部追蹤的穩定度,並且介紹了一個基於分群的手指指尖偵測方法,可以在手指追蹤失敗時用來還原手指座標。在手部追蹤完成後,我們利用手指指尖座標與我們的吉他和弦手勢資料庫比對,並且將結果整合到虛擬吉他系統中。在最後,我們設計了一個實驗讓一般的使用者在我們的虛擬吉他系統上演奏一首。而我們也分別在Kinect V1和Kinect V2彈上三首歌來討論兩種感測器的差異。結果說明了我們的方法在虛擬吉他系統上表現良好,而一般使用者也能簡單地學習我們的系統。


    Human Computer Interaction (HCI) is becoming a hot issue in computer science which makes people communicate with computers easier. A novel hand tracking method based on region search is presented in this paper. We adopt the concept of 3D point clouds to further improve the stability of hand tracking. Also, the cluster-based finger detection is introduced in this paper, which can be used for restoring finger position if finger tracking failed. After hand tracking finished, we use the position of fingertips to match our hand gesture with our guitar hand gesture database, and integrate proposed method into our virtual guitar system. At last, we setup an experiment which allows general users to play a song with our virtual guitar system. And we also test our system by playing three songs on both Kinect V1 and Kinect V2 sensor and discuss their difference. The result shows that our method performs well on virtual guitar system and general users can learn our system easily.

    摘要 i Abstract ii Acknowledgements iii Contents iv List of Figures vii List of Tables ix Chapter 1. Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Thesis Organization 3 Chapter 2. Related Works 4 2.1 Musical applications on Kinect 4 2.1.1 3D Music Control interface 4 2.1.2 A Gesture-Based Game for Teaching Music to Children 4 2.2 The analysis of existing method 5 2.2.1 Real-time Markerless Kinect based Finger Tracking and Hand Gesture Recognition for HCI 5 2.2.2 A robust method of detecting hand gestures using depth sensors 5 2.2.3 3D fingertips and palm tracking in depth image sequences 6 2.2.4 Efficient Model-based 3D Tracking of Hand Articulations using Kinect 7 2.3 Kinect V1 vs Kinect V2 8 2.4 Acoustic fingerprint 9 2.5 MIDI protocol 10 Chapter 3. Proposed method 13 3.1 Depth Frame and Point Clouds 14 3.2 Reduce Data Usage 16 3.2.1 Build Lookup Table for Hand Data 16 3.2.2 Down Sampling Hada Data 18 3.3 Center Point of Hand Palm and Wrist Removement 19 3.4 Hand Flipping Detection 20 3.5 Finger Detection 21 3.6 Finger Tracking 24 3.7 Finger Labeling 27 3.8 Guitar Chord Gesture Recognition 30 Chapter 4. Experimental Results and Discussions 33 4.1 Preliminary Results 33 4.2 Experiment Setup 34 4.2.1 Chord Testing 35 4.2.2 Note Testing 36 4.2.3 Song List and Testing Machine 37 4.2.4 Comparsion Tools and Method 38 4.2.5 Preparation of hand database 44 4.3 Experiment Results 45 4.3.1 Self Testing 45 4.3.2 User Testing 46 4.3.3 Benchmark Testing 48 Chapter 5. Conclusions and Future Works 49 5.1 Conclusions 49 5.2 Future Works 53 References 58

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    [15] http://www.midomi.com/
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    [24] https://duo3d.com/product/duo-minilx-lv1#tab=specs

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