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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電機工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43772
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dc.contributor.advisor傅立成(Li-Chen Fu)
dc.contributor.authorYi-Ru Chenen
dc.contributor.author陳羿如zh_TW
dc.date.accessioned2021-06-15T02:28:12Z-
dc.date.available2012-08-20
dc.date.copyright2009-08-20
dc.date.issued2009
dc.date.submitted2009-08-17
dc.identifier.citation[1] R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, 'Detecting moving objects, ghosts, and shadows in video streams,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 25, pp. 1337-1342, 2003.
[2] N. Jojic, M. Turk, and T. S. Huang, 'Tracking self-occluding articulated objects in dense disparity maps,' in Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, 1999, pp. 123-130 vol.1.
[3] J. K. Aggarwal and Q. Cai, 'Human motion analysis: a review,' in Nonrigid and Articulated Motion Workshop, 1997. Proceedings., IEEE, 1997, pp. 90-102.
[4] D. M. Gavrila, 'The visual analysis of human movement: A survey,' Computer Vision and Image Understanding, vol. 73, pp. 82-98, 1999.
[5] T. B. Moeslund and E. Granum, 'A survey of computer vision-based human motion capture,' Computer Vision and Image Understanding, vol. 81, pp. 231-268, 2001.
[6] T. B. Moeslund, A. Hilton, and V. Kruger, 'A survey of advances in vision-based human motion capture and analysis,' Computer Vision and Image Understanding, vol. 104, pp. 90-126, 2006.
[7] J. J. Wang and S. Singh, 'Video analysis of human dynamics--a survey,' Real-Time Imaging, vol. 9, pp. 321-346, 2003.
[8] W. Liang, T. Tieniu, N. Huazhong, and H. Weiming, 'Silhouette analysis-based gait recognition for human identification,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 25, pp. 1505-1518, 2003.
[9] L. Yi-Tzu, C.-M. Huang, C. Yi-Ru, and F. Li-Chen, 'Real-time face tracking and pose estimation with partitioned sampling and relevance vector machine,' in Robotics and Automation, 2009. ICRA '09. IEEE International Conference on, 2009, pp. 453-458.
[10] J. Lee, J. Chai, P. S. A. Reitsma, J. K. Hodgins, and N. S. Pollard, 'Interactive control of avatars animated with human motion data,' ACM Transactions on Graphics (TOG) archive, vol. 21, pp. 491-500, 2002.
[11] K. Dong-Wan and J. Ohya, 'Estimating Postures of a human wearing a multiple-colored suit based on color information processing,' in Multimedia and Expo, 2003. ICME '03. Proceedings. 2003 International Conference on, 2003, pp. I-261-4 vol.1.
[12] N. Dalal and B. Triggs, 'Histograms of oriented gradients for human detection,' in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, pp. 886-893 vol. 1.
[13] M. Isard and A. Blake, 'CONDENSATION—Conditional Density Propagation for Visual Tracking,' International Journal of Computer Vision, vol. 29, pp. 5-28, 1998.
[14] N. Gordon, D. Salmond, and A. Smith, 'Novel approach to nonlinear/non-Gaussian Bayesian state estimation,' IEE Proceedings, vol. 140, pp. 107-113, 1993.
[15] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, 'A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,' Signal Processing, IEEE Transactions on, vol. 50, pp. 174-188, 2002.
[16] A. Doucet, S. Godsill, and C. Andrieu, 'On sequential Monte Carlo sampling methods for Bayesian filtering,' Statistics and Computing, vol. 10, pp. 197-208, 2000.
[17] J. S. Liu and R. Chen, 'Sequential Monte Carlo Methods for Dynamic Systems,' Journal of the American Statistical Association, vol. 93, pp. 1032-1044, 1998.
[18] N. Gordon, 'Bayesian methods for tracking,' Ph. D. Thesis, Imperial College, University London, 1994.
[19] J. MacCormick and M. Isard, 'Partitioned sampling, articulated objects and interface-quality hand tracking,' Lecture Notes in Computer Science, vol. 1843, pp. 3-19, 2000.
[20] J. MacCormick and A. Blake, 'A probabilistic exclusion principle for tracking multiple objects,' in Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, 1999, pp. 572-578 vol.1.
[21] J. Deutscher, A. Blake, and I. Reid, 'Articulated body motion capture by annealed particle filtering,' in Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, 2000, pp. 126-133 vol.2.
[22] Q. Wang, L. Xie, J. Liu, and Z. Xiang, 'Enhancing particle swarm optimization based particle filter tracker,' Computational Intelligence, pp. 1216-1221, 2006.
[23] J. J. Pantrigo, A. S. Montemayor, and R. Cabido, 'Scatter Search Particle Filter for 2D Real-Time Hands and Face Tracking,' Image Analysis and Processing, pp. 953-960, 2005.
[24] P. Perez, J. Vermaak, and A. Blake, 'Data fusion for visual tracking with particles,' Proceedings of the IEEE, vol. 92, pp. 495-513, 2004.
[25] J. Deutscher, A. Davison, and I. Reid, 'Automatic partitioning of high dimensional search spaces associated with articulated body motion capture,' in Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, 2001, pp. II-669-II-676 vol.2.
[26] R. Navaratnam, A. Thayananthan, P. Torr, and R. Cipolla, 'Hierarchical part-based human body pose estimation,' in Pattern Analysis, Statistical Modelling and Computational Learning Oxford, UK, 2005.
[27] D. LeGall, 'MPEG: A video compression standard for multimedia applications,' Communications of the ACM, vol. 34, pp. 46-58, 1991.
[28] M. Yokoyama and T. Poggio, 'A contour-based moving object detection and tracking,' in 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005, pp. 271-276.
[29] J. Schmudderich, V. Willert, J. Eggert, S. Rebhan, C. Goerick, G. Sagerer, and E. Korner, 'Estimating Object Proper Motion Using Optical Flow, Kinematics, and Depth Information,' Systems, Man, and Cybernetics, Part B, IEEE Transactions on, vol. 38, pp. 1139-1151, 2008.
[30] G. N. Desouza and A. C. Kak, 'Vision for mobile robot navigation: a survey,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 237-267, 2002.
[31] S. Jianbo and C. Tomasi, 'Good features to track,' in Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on, 1994, pp. 593-600.
[32] B. D. Lucas and T. Kanade, 'An Iterative Image Registration Technique with an Application to Stereo Vision,' in Proceedings of Imaging Understanding Workshop, 1981, pp. 121-130.
[33] V. Eric and J. G. Leonidas, 'Optimally combining sampling techniques for Monte Carlo rendering,' in Proceedings of the 22nd annual conference on Computer graphics and interactive techniques: ACM, 1995.
[34] S. Fan, 'Sequential Monte Carlo Methods for Physically based Rendering,' Ph. D at Univeristy of Wisconsin-Madison, 2006.
[35] S. Waldherr, R. Romero, and S. Thrun, 'A Gesture Based Interface for Human-Robot Interaction,' Autonomous Robots, vol. 9, pp. 151-173, 2000.
[36] R. Stiefelhagen, C. Fugen, R. Gieselmann, H. Holzapfel, K. Nickel, and A. Waibel, 'Natural human-robot interaction using speech, head pose and gestures,' in IEEE/RSJ International Conference on Intelligent Robots and Systems Proceedings, 2004, pp. 2422-2427.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43772-
dc.description.abstract在這篇論文中描述了一個利用單眼相機人體上半身姿態追蹤系統。由於人體模型使用高維度的狀態空間來表示,因此利用分割取樣(partitioned sampling)降低狀態的維度,再以粒子濾波器(particle filter)來估測狀態值,其中所使用的觀測資料為影像中時間上與空間上的資訊。當進行人機互動時,靜止的單眼相機無法由影像中得到足夠的資訊,因此可以利用移動相機平台至較好的觀測位置,得到足夠的資訊來修正系統的估測值。最後,透過實驗來驗證此系統的整體效能及可靠性。zh_TW
dc.description.abstractThis thesis presents an upper body tracking method with a monocular camera. The human model is defined in a high dimensional state space. We hereby propose a hierarchical structure model to solve the tracking problem by particle filter with partitioned sampling. The spatial and temporal information from the image is used to track the human body and estimate the human posture. When doing the human-robot interaction, a static monocular camera may not get plenty of information from the 2D images, so we must move the camera platform to a better position for acquiring more enriched image information. The proposed upper body tracking technique will then self-adjust to estimate the human posture during the camera movement. To validate the effectiveness of the proposed tracking approach, extensive experiments have been performed, of which the result appear to be quite promising.en
dc.description.provenanceMade available in DSpace on 2021-06-15T02:28:12Z (GMT). No. of bitstreams: 1
ntu-98-R96921005-1.pdf: 1660630 bytes, checksum: b576e5305f479b6e7d69606030bb4893 (MD5)
Previous issue date: 2009
en
dc.description.tableofcontents摘要 I
ABSTRACT II
CONTENTS III
LIST OF FIGURES V
LIST OF TABLES VII
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 2
1.2 RELATED WORKS 3
1.3 CONTRIBUTION 5
1.4 THESIS ORGANIZATION 6
CHAPTER 2 PRELIMINARIES 7
2.1 BAYESIAN FILTER 7
2.2 PARTICLE FILTER 12
2.2.1 Monte Carlo Integration 13
2.2.2 Sequential Importance Sampling (SIS) Particle Filter 14
2.2.3 Resampling and Degeneracy Problem 17
2.2.4 Sampling Importance Resampling (SIR) Particle Filter 20
2.2.5 Impoverishment Phenomenon 21
2.3 PARTITIONED SAMPLING 22
2.4 OPTICAL FLOW 26
CHAPTER 3 UPPER BODY TRACKING 29
3.1 HUMAN MODEL DEFINITION 29
3.2 POSTURE INITIALIZATION 36
3.3 PARTITIONED SAMPLING FOR HUMAN MODEL 38
3.4 SIR PARTICLE FILTER FOR FACE TRACKER 39
3.5 MULTIPLE IMPORTANCE SAMPLING (MIS) 40
3.5.1 The Balance Heuristic 41
3.6 MIS PARTICLE FILTER FOR ARM TRACKER 44
3.6.1 Latest Posterior 46
3.6.2 Inverse Kinematics 47
3.6.3 Line Detector 49
3.7 UPPER BODY TRACKING SYSTEM 51
CHAPTER 4 IMPLEMENTATION 53
4.1 LIKELIHOOD FUNCTION FOR PARTICLE FILTER 53
4.1.1 Color Histogram 54
4.1.2 Enhanced Edge Contour 56
4.1.3 Angle on Image Plane 61
4.1.4 Overall Likelihood 62
4.2 HUMAN ROBOT INTERACTION 63
4.3 MOTION STRATEGY 65
CHAPTER 5 EXPERIMENTAL RESULT 67
5.1 ENVIRONMENT DESCRIPTION 67
5.2 RESULTS OF UPPER BODY TRACKING 68
5.3 RESULTS OF MOTION CAMERA 71
5.4 RESULTS OF HUMAN ROBOT INTERACTION 73
5.5 DISCUSSIONS 75
CHAPTER 6 CONCLUSION AND FUTURE WORK 77
6.1 CONCLUSION 77
6.2 FUTURE WORK 78
REFERENCE 79
dc.language.isoen
dc.subject上半身追蹤zh_TW
dc.subject粒子濾波器zh_TW
dc.subject影像追蹤zh_TW
dc.subjectupper body trackingen
dc.subjectparticle filteren
dc.subjectvisual trackingen
dc.title人體上半身姿態追蹤系統應用於移動式平台之人機互動zh_TW
dc.titleHuman Robot Interaction with Motion Platformen
dc.typeThesis
dc.date.schoolyear97-2
dc.description.degree碩士
dc.contributor.oralexamcommittee羅仁權,宋開泰,范欽雄,王傑智
dc.subject.keyword粒子濾波器,影像追蹤,上半身追蹤,zh_TW
dc.subject.keywordparticle filter,visual tracking,upper body tracking,en
dc.relation.page81
dc.rights.note有償授權
dc.date.accepted2009-08-17
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept電機工程學研究所zh_TW
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