Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8653
標題: 基於電腦視覺之即時三維人體姿態特徵點估測
Vision-based Real-Time 3D Human Posture Significant Points Estimation
作者: 陳登昌
Chen, Teng-Chang
關鍵字: image segmentation;電腦視覺;human posture estimation;影像切割;姿態預估
出版社: 電機工程學系所
引用: [1] H. Fujiyoshi and A. J. Lipton, “Real-time human motion analysis by image skeletonization,” Proc. IEEE Workshop on Applications of Computer Vision, Oct, 1998, pp. 15 - 21. [2] I. C. Chang and C. L. Huang, “The model-based human body motion analysis system,” Image and Computing, vol. 18, pp. 1067-1083, 2000. [3] B. Guo and M. S. Nixon, “Gait feature subset selection by mutual information,” IEEE Trans. Syst., Man, and Cyber.,- Part A: Systems and Humans, vol. 39, no. 1, pp. 36-46, Jan. 2009. [4] I. Haritaoglu, D. Harwood and L. S. Davis, “ real-time surveillance of people and their activities,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809 - 830, Aug. 2000. [5] T. S. Barger, D. E. Brown, and M. Alwan, “Health-status monitoring through analysis of behavioral patterns,” IEEE Trans. Syst., Man, and Cyber.,- Part A: Systems and Humans, vol. 35, no. 1, pp. 22-27, Jan. 2005. [6] D. Anderson, R. H. Luke, J. M. Keller, M. Skubic, M. J. Rantz, and M. A. Aud, “Modeling human activity from voxel person using fuzzy logic,” IEEE Trans. Fuzzy Systems, vol. 17, no. 1, pp. 39-49, Feb. 2009. [7] R. Cucchiara, C. Grana, A. Prati, and R. Vezzani, “Probabilistic posture classification for human-behavior analysis,” IEEE Trans. Syst., Man, and Cyber.,- Part A: Systems and Humans, vol. 35, no. 1, pp. 42-54, Jan. 2005. [8] J. S. Hu, T. Z. Su, and P. C. Lin, “3-D human posture recognition system using 2-D shape features,” Proc. of IEEE International Conference on Robotics and Automation, pp. 3933-3938, 2007. [9] C. F. Juang and C. M. Chang, “Human body posture classification by neural fuzzy network and its application to home care system,” IEEE Trans. Syst., Man, and Cyber., Part A: Systems and Humans, vol. 37, no. 6, pp. 984-994, Nov. 2007. [10] M. Singh, A. Basu, and M. Mandal, “Human activity recognition based on silhouette directionality,” IEEE Trans. Circuits and Systems for Video Technology, vol. 18, no. 9, pp. 1280-1292, Sept. 2008. [11] T. Noma, I. Oishi, H. Futsuhara, H. Baba, T. Ohashi, and T. Ejima, “Motion generator approach to translating human motion from video to animation,” Proc. 7th Pacific Conf. Computer Graphics and Applications, pp. 50-58, Oct. 1999. [12] K. Singh, J. Ohya, and R. Parent, “Human figure synthesis and animation for virtual space teleconferencing,” Proc. Annual Int. Symp. Virtual Reality, pp. 118-126, March, 1995. [13] K. Watanabe and M. Hokari, “Kinematical analysis and measurement of sports form,” IEEE Trans. Syst., Man, and Cyber., Part A: Systems and Humans, vol. 36, no. 3, pp. 549- 557, 2006. [14] F. Ofli, C. Canton-Ferrer, J. Tilmanne, Y. Demir, E. Bozkurt, Y. Yemez, E. Erzin, and A. M. Tekalp, “Audio-driven human body motion analysis and synthesis,” Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, pp. 2233 - 2236, 2008. [15] K. Takahashi, T. Sakaguchi, and J. Ohya, “Remarks on a real-time 3D human body posture estimation method using trinocular images,” Proc. Of 15th Int. Conf. Pattern Recognition, vol. 4, pp. 693-697, 2000. [16] G.K.M. Cheung, T. Kanade, J. Y. Bouguet, and M. Holler, “A real time system for robust 3D voxel reconstruction of human motions,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 714 - 720, June 2000. [17] R. Rosales, M. Siddiqui, J. Alon, and S. Sclaroff, “Estimating 3D body pose using uncalibrated cameras,” Proc. of IEEE Computer Society Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 821 - 827, 2001. [18] I. Mikic, M. Trivedi, E. Hunter, and P. Cpsman, “Articulated body posture estimation from multi-camera voxel data,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 455 - 460, 2001. [19] T. Matsuyama, X. Wu, T. Takai, and S. Nobuhara, “Real-time dynamic 3D object shape reconstruction and high-fidelity texture mapping for 3-D video,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 3, pp. 357-369, March 2004. [20] K. Takahashi, Y. Nagasawa, and M. Hashimoto, “Remarks on 3D human posture estimation system using simple multi-camera system”, Proc. IEEE Int. Conf. Syst., Man, and Cyber., pp. 1962-1967, 2006. [21] S. Y. Chen, S. Y. Ma, and L. G. Chen, “Efficient moving object segmentation algorithm using background registration technique,” IEEE Trans. Circuits and Systems for Video Technique, vol. 12, no. 7, pp. 577-586, July 2002. [22] C. Kim, J. Cho, and Y. Lee, “The relational properties among results of background subtraction”, Proc. of Int. Conf. on Advanced Communication Technology, vol. 3, pp. 1887-1890, 2008. [23] C. F. Juang, C. M. Chang, J. R. Wu, and D. M. Lee, “Computer vision-based human body segmentation and posture estimation,” IEEE Trans. Syst., Man, and Cyber., Part A: Systems and Humans, vol. 39, no. 1, pp. 119-133, Jan. 2009. [24] H.D. Cheng, X.H. Jiang, Y. Sun and J. Wang, “Color image segmentation: advances and prospects”, Pattern Recognition, vol. 34, pp. 2259-2281, 2001. [25] Q. Zhou and J.K. Aggarwal, “Tracking and classifying moving objects from video”, Proc. IEEE Int. Workshop Performance Evaluation of Tracking and Surveillance, pp. 52-59, Dec. 2001. [26] Razali M.T. and Adznan B.J., “Detection and classification of moving object for smart vision sensor,” Proc. Information and Communication Technologies vol. 1, pp. 733-737, 2006. [27] T. Stehle, D. Truhn, T. Aach, C. Trautwein, and J. Tischendorf, “Camera calibration for fish-eye lenses in endoscopy with an application to 3D reconstruction”, Proc. of IEEE international Symposium on Biomedical Imaging, pp. 1176-1179, 2007. [28] K. Cornelis, F. Verbiest, and L.V. Gool, “Drift detection and removal for sequential structure from motion algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, pp. 1249-1259, 2004. [29] W. N. Martin and J. K. Aggarwal, “Volumetric description of objects from multiple views”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 150-158, 1987. [30] M. Potmesil, “Generating octree models of 3D objects from their silhouette in a sequence of images”, Computer Vision, Graphics, and Image Processing, vol. 40, pp. 1-29, 1987. [31] P. Strivasan, P. Lang, and S. Hackwood, “Computational geometric methods in volumetric intersections for 3D reconstruction”, Patter Recognition, vol. 28, no. 8, pp. 843-857, 1990. [32] S. Iwasawa, K. Ebihara, J. Ohya, and S. Morishima, “Real-time estimation of human body posture from monocular thermal images,” Proc. of IEEE Computer Society Conf. Computer Vision and Pattern Recognition, pp. 15-20, 1997 [33] A.K. Jain, “Fundamentals of digital image processing”, Prentice-Hall, Englewood Cliffs, NJ, 1989.
摘要: 
本論文提出一種即時三維人體姿態的預估方法,此方法藉由兩部攝影機擷取的人體輪廓資訊,在三維空間中定位人體的個別重要部位點,這些部位包含頭、身體的中心點、雙腳尖末端以及雙手末端點。首先,提出一種角度補償的色彩三原色分割演算法(AC-RGB segmentation),此法將人體與背景從一連串的影像中區分出來,並且,利用顏色夾角判斷初步擷取的部份,排除陰影的背景部份;最後經由一系列的影像處理,在兩部相異之攝影機畫面中,獲得個別完整的人體輪廓。對於個別攝影機影像上的二維人體姿態預估,則是藉由人體周圍輪廓之凸點以及幾何特徵來判斷。最後,經由二維人體姿態估測結果,使用重點體積交集法(SPVI)建構出三維人體的重要部位點。實驗結果顯示,角度補償的色彩三原色分割演算法(AC-RGB segmentation),相較於其他不同色彩模式的影像切割方法,有較好的切割結果。本論文也建構一個即時三維人體姿態預測系統,來驗證提出的方法是可行的。

This thesis proposes a real-time 3D human body posture estimation method to locate different significant body points in 3D space by using 2D extracted body contours from two cameras. The located 3D significant body points include the head, center of the body, tips of the feet, and tips of the hands. First, an Angle-Compensated RGB (AC-RGB) segmentation algorithm is proposed to distinguish the human body from background from a sequence of images in RGB model and reduce shadow influence by considering the included angle between pixels that are classified within a moving object and background. After segmentation, a sequence of image processing approaches then creates a complete contour of the human body for each of the two images from different cameras. Posture estimation in 2D is performed on each of the two images by using contour convex points and body geometrical characteristics. Finally, a Significant Point Volume Intersection (SPVI) method is proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed AC-RGB segmentation approach shows better performance than other segmentation approaches in different color models. This thesis also sets up a real-time 3D posture estimation system to verify the effectiveness of the proposed approach.
URI: http://hdl.handle.net/11455/8653
其他識別: U0005-2607200922205200
Appears in Collections:電機工程學系所

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