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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 鄭士康(Shyh-Kang Jeng) | |
dc.contributor.author | Chih-Chun Yang | en |
dc.contributor.author | 楊智鈞 | zh_TW |
dc.date.accessioned | 2021-06-17T00:01:51Z | - |
dc.date.available | 2017-07-27 | |
dc.date.copyright | 2012-07-27 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-07-14 | |
dc.identifier.citation | [1] D. Roetenberg, H. Luinge and P. Slycke, “Xsens MVN: full 6DOP human motion tracking using miniature inertial sensors,” Xsens Technologies, April 2009.
[2] C. Stauffer and W.E.L. Grimson, “Adaptive background mixture models for real-time tracking,” IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, August 1999. [3] J. Shotton et al., “Real-time human pose recognition in parts from single depth images,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 1297-1304, June 2011. [4] 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, no. 2, pp. 90-126, November 2006. [5] C. Keskin, F. Kirac, Y. E. Kara and L. Akarun, “Real time hand pose estimation using depth sensors,” IEEE ICCV Workshops, pp. 1228-1234, 2011. [6] C. Plagemann, V. Ganapathi, D. Koller and S. Thrum, “Real-time identification and localization of body parts from depth images,” IEEE International Conference on Robotics and Automation, pp. 3108-3113, May 2010. [7] V. Ganapathi, C. Plagemann, D. Koller and S. Thrun, “Real time motion capture using a single depth time-of-flight camera,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 755-762, June 2010. [8] A. Baak, M. Muller, G. Bharaj, H.-P. Seidel and C. Theobalt, “A data-driven approach for real-time full body pose reconstruction from a depth camera,” IEEE International Conference on Computer Vision, pp. 1092-1099, November 2011. [9] L. A. Schwarz, A. Mkhitaryan, D. Mateus and N. Navab, “Human skeleton tracking from depth data using geodesic distances and optical flow,” Image and Vision Computing, vol. 30, no. 3, pp. 217-226, March 2012. [10] Y. Zhu, B. Dariush and K. Fujimura, “Controlled human pose estimation from depth image streams,” IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-8, June 2008. [11] S. Ivekovic, E. Trucco and Y. R. Petillot, “Human body pose estimation with particle swarm optimization,” Evolutionary Computation, vol. 16, no. 4, pp. 509-528, winter 2008. [12] M. Siddiqui and G. Medioni, “Human pose estimation from a single view point, real-time range sensor,” IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-8, June 2010. [13] P.-C. Huang and S.-K. Jeng, “Human body pose recognition from a single view depth camera,” M.A. thesis, National Taiwan University, Taiwan, R.O.C., 2012. [14] M. Straka, S. Hauswiesner, M. Ruther and H. Bischof, “Skeletal graph based human pose estimation in real-time,” Proc. BMVC, BMVA Press, 2011, pp.69.1-69.2. [15] I. Oikonomidis, N. Kyriazis and A. A. Argyros, “Full DOF tracking of a hand interacting with an object by modeling occlusions and physical constraints,” IEEE International Conference on Computer Vision, pp. 2088-2095, November 2011. [16] D. L. Ly, A. Saxena and H. Lipson, “Pose estimation from a single depth image for arbitrary kinematic skeletons,” RGB-D Workshop at RSS, 2011. [17] A. Appel, 'Some techniques for shading machine renderings of solids,' AFIPS Proceedings, pp. 37-45, 1968. [18] J. Kennedy, R. C. Eberhart, “Particle swarm optimization,” IEEE International Conference on Neural Network, vol. 4, pp. 1942-1948, November 1995. [19] R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39-43, October 1995. [20] Z.-H. Zhan, J. Zhang, Y. Li and H. S.-H. Chung, “Adaptive particle swarm optimization,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 39, pp. 1362-1381, December 2009. [21] C. Bastos-Filho, M. O. Junior and D. Nascimento, “Running particle swarm optimization on graphic processing units,” in Search Algorithms and Applications, Nashat Mansour (Ed.), ISBN: 978-953-307-156-5, InTech, 2011. [22] NVIDIA, “NVIDIA CUDA C programming guide v4.2,” NVIDIA, 2012, available at: http://developer.nvidia.com/cuda-downloads. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65727 | - |
dc.description.abstract | 在本篇論文中,我們提出一個可在CUDA平台上實作的人體姿態辨識方法。此演算法僅需要單視角深度影像,不需要前人研究中所需的彩色影像或多視角影像。此演算法包含以下特點,我們設計了一個由兩個橢圓柱及九個橢球構成且擁有32個自由度的人體模型。也提出了一個改良版的粒子群優化演算法架構以解決我們面對的最佳化問題。最後,充分利用演算法的高度平行性,將此方法實作在CUDA平台上以達到即時運算的效能。我們以微軟Kinect作為深度相機,並用NVIDIA GTS450作為主要的計算處理器。實驗結果顯示,本論文提出的方法可有效的解決此領域常見之自我遮蔽的問題,憑藉著圖形處理器的計算能力,此系統可做即時性運算(每秒鐘12至33幀)。 | zh_TW |
dc.description.abstract | In this thesis, we propose a human pose estimation algorithm and implement the algorithm on CUDA platform. The proposed algorithm needs only single-view depth image as input, unlike some former works which take color images or multi-view images. The proposed algorithm contains the following features: first, a 32 degree-of-free model composed of two elliptic cylinder and nine ellipsoids is adopted to formulate an optimization problem. Second, a modified particle swarm optimization (PSO) scheme is applied to solve the optimization problem. And this highly parallel algorithm is suitable to be implemented on CUDA platform to achieve real-time performance. We use the Microsoft Kinect as depth sensor and use the NVIDIA GTS450 as computing device. The experimental result shows that the proposed algorithm is robust enough to overcome the self-occlusion which is the common difficulty in this area. And with the aid of this GPU, this algorithm can work in real-time (12-33 fps). | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T00:01:51Z (GMT). No. of bitstreams: 1 ntu-101-R99942048-1.pdf: 2353781 bytes, checksum: a9448828b564430805ca65b7babad02f (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES viii LIST OF TABLES xi Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Challenges in Human Pose Estimation 3 1.3 Research Contributions 3 1.4 Thesis Organization 3 Chapter 2 Related Work 4 2.1 Feature-based Approach 4 2.2 Model-based Approach 5 2.3 Comparison with Our method 6 Chapter 3 Background 7 3.1 Ray-casting Rendering 7 3.2 Particle Swarm Optimization 7 3.2.1 Conventional PSO 8 3.2.2 Adaptive PSO 9 3.2.3 Topology of PSO 10 3.3 CUDA Platform 11 Chapter 4 Proposed Method 12 4.1 Human model 12 4.1.1 Kinematic Chain 12 4.1.2 Degrees of Freedom of the Model 13 4.1.3 Fitness Evaluation 14 4.2 Proposed PSO Scheme 15 4.2.1 Modified APSO 16 4.2.2 Random Coordinate Rotation 17 4.2.3 Crossover Mechanism 19 4.2.4 Stagnation Removal 20 Chapter 5 CUDA Implementation 21 5.1 Parallel Ray-casting 21 5.2 Implementation of PSO 22 5.3 Efficiency of Implementation 23 Chapter 6 Experiments and Discussions 24 6.1 Parameter Selection for PSO 24 6.2 Quantitative Evaluation of the Proposed Method 25 6.2.1 Two Evaluation Criteria 25 6.2.2 Evaluation of Single Frame Pose Estimation 26 6.2.3 Evaluation of Sequence Pose Estimation 29 6.3 Qualitative Evaluation 31 6.3.1 Result of Single Frame Pose Estimation 31 6.3.2 Result of Sequence Pose Estimation 32 6.4 Computation Time 33 6.5 Discussion 33 Chapter 7 Conclusions 35 REFERENCES 36 | |
dc.language.iso | en | |
dc.title | 利用深度圖與粒子群優化演算法之人體動作偵測 | zh_TW |
dc.title | Human Pose Estimation Using Depth Map and Particle Swarm Optimization | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 洪一平(Yi-Ping Hung),丁建均(Jian-Jiun Ding) | |
dc.subject.keyword | 人體姿勢辨識,粒子群優化演算法,深度感測器,圖形處理器編程, | zh_TW |
dc.subject.keyword | Human Pose Estimation,Particle Swarm Optimization,Depth Sensor,GPU Programming, | en |
dc.relation.page | 38 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-07-16 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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