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研究生:穆罕德
研究生(外文):Mohammad Firdaus Ardiansyah
論文名稱:瑜珈姿態辨識-使用多重KINECT
論文名稱(外文):Multiple Kinects Motion Sensing for Yoga
指導教授:葉士青
指導教授(外文):Yeh Shih-Ching
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:39
外文關鍵詞:HumanYogabodytrackingMultipleKinectMultipleKinect
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ABSTRACT
Human motion tracking is receiving increasing attention from researchers of different fields of study nowadays. The interest is motivated by a wide range of applications, such as wireless healthcare, surveillance, and human-computer interaction. A complete model of human consists of both the movements and the shape of the human body skeleton. There are so many ways to track human motion, from marker sensing, marker less, optical, etc. Kinect sensor developed by Microsoft had done human tracking in 3d perspective and widely used in both in commercial and experiments purpose. However this Kinect method tracking by estimating depth image yield good result for simple gestures. In this study a novel approach is proposed to track complicated movement such as yoga movement with three Kinects in order to overcome kinect’s limitation.


摘 要
人體動作偵測最近在不同領域的研究中正逐漸變成一個令人關注的議題。無線健康照護、監控系統、人機互動等應用為促成此篇論文的動機。一個完整的人體模型包含動作以及骨架的形狀。 有許多方法可以用來追蹤人體動作,配戴感測器、光學追蹤等。微軟開發的kinect可以在三維空間中完成人體姿態辨識,而且在商業、實驗用途都被廣泛地使用。然而kinect藉由估計深度圖像來追蹤的方法只對簡單的姿勢才能有較好的結果。本論文提出了一個新的方法,利用三部kinect克服單部kinect的限制,使其可以追蹤如瑜珈般複雜的動作。
NATIONAL CENTRAL UNIVERSITY iii
DEPARTMENT OF COMPUTER SCIENCE AND iii
INFORMATION ENGINEERING iii
摘 要 vi
ABSTRACT vii
Keywords : Human body tracking, Kinect, Multiple Kinect, Yoga Motion vii
TABLE OF CONTENT viii
LIST OF TABLE x
LIST OF FIGURE xi
ACKNOWLEDGEMENT xii
CHAPTER 1: INTRODUCTION 1
1.1. Background and Motivation 1
1.2. Research Object 2
1.3. Structure of the thesis 2
CHAPTER 2: LITERATURE REVIEW 3
2.1. Motion Tracking with Depth Camera (Kinect) 3
2.2. Toolkit for Kinect 5
2.2.1. OpenNI 5
2.2.2. Microsoft Kinect SDK 6
2.2.4. Comparation from Toolkits 8
2.3. Motion Tracking with Multiple Depth Cameras (Kinects) 9
2.4. Tracking Algorithm 11
CHAPTER 3: METHODOLOGY 12
3.1. Research design 12
3.2. System design 13
3.2.1. System Environment 13
3.2.2. Training system 13
3.2.3. Single Kinect System 16
3.2.4. Multiple Kinect System 17
3.2.5. Assessment system 20
3.3. Experiment Design 21
3.4. Measurement and Analysis 24
3.4.1. Performance assessment 24
3.4.2. Human perception assessment 24
CHAPTER 4: DATA ANALYSIS AND RESULTS 25
4.1. Performance Assessment. 25
4.2. Analysis and Measurement Human Behavior and Perception 28
CHAPTER 5: CONCLUSION 30
5.1. Conclusion 30
5.2. Future Work 30
References 31
Appendix 33
Questionnaire 35


References
1. Zhang, S.X., W.;Gong, J.;Yin,Y., A Novel Human Motion Tracking Approach Based on a Wireless Sensor Network. International Journal of Distributed Sensor Networks, 2013. Volume 2013, Article ID 636052.
2. Organicmotion, What is Motion Capture? url http://www.organicmotion.com/products/openstage/motion-capture (accessed July 19 2013), 2013.
3. Knies, R., Academics, Enthusiasts to Get Kinect SDK. url http://research.microsoft.com/en-us/news/features/kinectforwindowssdk-022111.aspx (accessed 2013-07-20), 2011.
4. Microsoft, Human Interface Guideline. Microsoft Kinect SDK Documentation, 2013.
5. Destructoid, The Limitations of Kinect. url http://www.destructoid.com/blogs/pneuma08/the-limitations-of-kinect-186724.phtml (accessed 2013-07-20), 2010.
6. Microsoft, Kinect. url: http://www.xbox.com/en-us/kinect (accessed May 27, 2013), 2010.
7. Webb, J. and J. Ashley, Beginning kinect programming with the microsoft kinect SDK. The expert's voice in Microsoft. 2012, New York: Apress. xvii, 306 p.
8. Cong, R.W., R., How It Works: Xbox Kinect. url: http://www.jameco.com/Jameco/workshop/howitworks/xboxkinect.html (accessed on june 9th 2013).
9. NI, O., OPEN NI. url: http://www.openni.org/ (accessed May 27, 2013), 2013.
10. PrimeSense, NiTE Middleware. url: http://www.primesense.com/solutions/nite-middleware/ (accessed june 9th 2013), 2013.
11. Microsoft, Kinect for Windows Feature. url http://www.microsoft.com/en-us/kinectforwindows/Discover/Features.aspx (accessed 2013-07-20), 2013.
12. Will, H., Kinect for Windows SDK beta vs OpenNI. url: http://labs.vectorform.com/2011/06/windows-kinect-sdk-vs-openni-2/ (accessed June 9th, 2013), 2011.
13. Cai, Q.A., J. K.;, Tracking Human Motion Using Multiple Cameras. IEEE, 1996. Proceedings of ICPR(1996).
14. Dockstader, S.T., M.;, Multiple Camera Tracking of Interacting and Occluded Human Motion. PROCEEDINGS OF THE IEEE, 2001. 89(2001).
15. Ian, W., Performance and Motion Capture Using Multiple Xbox Kinects. Rochester Institute of Technology, 2013, 2013.
16. Shotton, J., et al., Real-Time Human Pose Recognition in Parts from Single Depth Images. Communications of the Acm, 2013. 56(1): p. 116-124.
17. Moll, G.P.B., A.; Helten, T.; Muller, M.; Seidel, H.P.; Rosenhahn, B., Multisensor-Fusion for 3D Full-Body Human Motion Capture. IEEE, 2010.
18. Rosenhahn, B.K., R.; Metaxas, D., Human motion - understanding, modelling, capture and animation. Netherlands: Springer, 2008.
19. Gall, J., et al., Optimization and Filtering for Human Motion Capture. International Journal of Computer Vision, 2010. 87(1-2): p. 75-92.
20. Obdrzalek, S.K., G.; Ofli, F.; Bajcsy, R., Accuracy and Robustness of Kinect Pose Estimation in the Context of Coaching of Elderly Population. Annual International Conference of the IEEE EMBS, 2012.
21. Li, R., et al., 3D Human Motion Tracking with a Coordinated Mixture of Factor Analyzers. International Journal of Computer Vision, 2010. 87(1-2): p. 170-190.
22. Georgakopoulos, D.Y., W.Q., Circuit noise reduction by analogue lowpass filtering and data averaging. IEEE Electronics Letters Online No: 20010782, 2001.



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