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論文中文名稱:適用於近距離手部運動之遊戲平台設計 [以論文名稱查詢館藏系統]
論文英文名稱:The Design of a Game Platform for Close Range Hand Motion [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:電資學院
系所名稱:資訊工程系研究所
畢業學年度:102
出版年度:103
中文姓名:黃笠維
英文姓名:Li-Wei Huang
研究生學號:101598034
學位類別:碩士
語文別:中文
口試日期:2014-07-14
論文頁數:62
指導教授中文名:張厥煒
口試委員中文名:奚正寧;楊士萱
中文關鍵詞:人機介面手勢辨識Leap Motion
英文關鍵詞:Human-Computer InteractionHand Gesture RecognitionLeap Motion
論文中文摘要:近年來體感裝置推陳出新,廣泛的使用於遊戲領域,為了使玩家更融入遊戲情境,遊戲開發者設計出適合遊戲內容之動作,並提供手勢讓使用者操作選單介面,取代舊式搖桿控制器,以自然直覺的方式與系統溝通。
本論文著重近距離之手部動作,設計並實作一手勢動作偵測系統,透過Leap Motion提供的手部特徵資訊,追蹤手在空間中的位置、角度、方向、指尖位置等,利用這些資訊進行手勢動作偵測。系統之手勢辨識分為兩類,操控手勢和連續動作比對。操控手勢是利用定義好的手勢特徵比對,當偵測到畫面中出現定義之手勢時,計算並輸出對應數值,如兩手之距離;連續動作比對是錄製一連串的畫面,記錄手部動作軌跡,再以預先錄製之動作樣本進行比對,判定屬於哪一個動作。
使用系統提供的操控手勢於介面操作,再加上錄製的連續動作樣本,開發者可以快速完成一體感遊戲,本論文將在後面章節提出實作的遊戲以展示本系統之成果。
論文英文摘要:Recently, motion sensing input devices commonly used in games. In order to make players enjoy the game, the developers designed many funny motion and convenient gesture. These devices in place of old and make Human-Computer Interface friendlier.
This paper focus on close range hand motion, designed and implement a hand gesture detection system. Using Leap Motion to obtain hand features to recognize hand gesture. The hand features include position, angle, direction, position of fingertip, etc. The whole system divided into two parts. The first part is manipulation gesture detection system. It use predefined gesture feature value to detect, the system will output gesture info when gesture appear in frame. The second part is continuous motion recognition system. It used to recognize the motion recorded by developer.
Developer can use this system to build a motion sensing game fast. This paper will show a game to demonstrate the achievement of this system.
論文目次:摘要 i
ABSTRACT ii
誌 謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究範圍與限制 3
1.4 論文架構 3
第二章 相關研究與文獻探討 5
2.1 手部追蹤 5
2.2 靜態手勢辨識 6
2.3 動態手勢辨識 7
第三章 系統架構與流程 12
3.1 系統架構 12
3.2 操控手勢偵測器 13
3.3 手部動作比對與資料庫 15
第四章 操控手勢定義 17
4.1 手部骨架概述 17
4.2 手指狀態定義 19
4.3 手勢定義 21
4.3.1 抓取手勢 22
4.3.2 雙食指手勢 25
4.3.3 點擊手勢 28
4.3.4 指標手勢 32
第五章 手部動作比對 35
5.1 特徵挑選與處理 35
5.2 特徵相似度計算 38
5.3 樣本關鍵影格選取 39
5.4 動作比對 41
5.4.1 樣本比對器 41
5.4.2 動作序列比對 42
5.4.3 多樣本動作比對 45
第六章 實驗結果 47
6.1 實驗環境 47
6.2 手部動作比對實驗 48
6.2.1 相似度閥值對於關鍵影格選取數量的影響 48
6.2.2 相似度閥值對於辨識率的影響 51
6.2.3 動作比對時間效率 52
6.3 應用遊戲實作 53
第七章 結論與未來展望 58
7.1 結論 58
7.2 未來展望 58
參考文獻 60
論文參考文獻:[1] E. Gutzeit, M. Vahl, Z. Zhou and U. V. Lukas, “Skin Cluster Tracking and Verification for Hand Gesture Recognition,” International Symposium on Image and Signal Processing and Analysis (ISPA), 2011, pp.241-246.
[2] R. Y. Wang and J. Popovic, “Real-Time Hand-Tracking with a Color Glove,” ACM Transactions on Graphics, vol. 28, no.3, 2009, pp.505-513.
[3] K. Oka, Y. Sato, and H. Koike, “Real-Time Tracking of Multiple Fingertips and Gesture Recognition for Augmented Desk Interface Systems,” IEEE 5th International Conference on Automatic Face and Gesture Recognition, 2002, pp.429.
[4] Z. Hang, R. Qiuqi and C. Houjinl, “A New Approach of Hand Tracking Based on Integrated Optical Flow Analyse,” IEEE 10th International Conference on Signal Processing (ICSP), 2010, pp.1194-1197.
[5] H. L. Lee, C. L. Hsu, C. C. Chen, J. S. Taur, and C. W. Tao, “Real-Time Hand Gesture Controlled Mouse Using Kinect,” 25th Computer Vision, Graphics, and Image Processing (CVGIP), Nantou, Taiwan, 2012, pp.189-197.
[6] G. Khurana, G. Joshi amd J. Kaur, “Static Hand Gestures Recognition System Using Shape Based Features,” Recent Advances in Engineering and Computational Sciences (RAECS), 2014, pp.1-4.
[7] C. W. Tan, S. W. Chin and W. X. Lim, “Game-Based Human Computer Interaction Using Gesture Recognition for Rehabilitation,” Control System, Computing and Engineering (ICCSCE), 2013, pp.344-349.
[8] Z. Liang, N. Sun and M. Cao, “Recognition of Static Human Gesture Based on Radiant-Projection-Transform and Fourier-Transform,” Image and Signal Processing (CISP), vol.4, 2008, pp.635-640.
[9] G. Simion, V. Gui and M. Otesteanu, “Finger Detection Based on Hand Contour and Color Information,” IEEE 6th International Symposium on Applied Computational Intelligence and Informatics, 2011, pp.19-21.
[10] A. Ramaoorthy, N. Vaswani, S. Chaudhury, and S. Banerjee, “Recognition of Dynamic Hand Gestures,” Pattern Recognition Society, Grenoble, 2003, pp.2069-2081.
[11] X. Wang and G. Dai, “A Novel Method to Recognize Complex Dynamic Gesture by Combining HMM and FNN Models,” Computational Intelligence in Image and Signal Processing (CIISP), 2007, pp.13-18.
[12] Wikipedia, “Hidden Markov model,” online available at: https://en.wikipedia.org/wiki/Hidden_Markov_model
[13] Wikipedia, “Dynamic time warping,” online available at: http://en.wikipedia.org/wiki/Dynamic_time_warping
[14] B. Hartmann, N. Link, “Gesture Recognition with Inertial Sensors and Optimized DTW Prototypes,” 7th Sound and Music Computing Conference (SMC), 2010, pp.2102-2109.
[15] K. Wang, Y. Wang, Z. Zhang, “On-Line Signature Verification Using Segment-to-Segment Graph Matching,” 11th International Conference on Document Analysis and Recognition (ICDAR), 2011, pp.804-808.
[16] R. Verma and A. Dev, “Vision Based Hand Gesture Recognition Using Finite State Machines,” International Conference on Ultra Modern Telecommunications & Workshops, 2009, pp.1-6.
[17] Leap Motion, [Online]. Available: http://leapmotion.com

[18] Wikipedia, “Dynamic time warping,” online available at: http://en.wikipedia.org/wiki/Dynamic_time_warping
論文全文使用權限:同意授權於2019-08-28起公開