請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/24387
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃漢邦 | |
dc.contributor.author | Chun-Ting Lin | en |
dc.contributor.author | 林俊廷 | zh_TW |
dc.date.accessioned | 2021-06-08T05:24:08Z | - |
dc.date.copyright | 2005-07-27 | |
dc.date.issued | 2005 | |
dc.date.submitted | 2005-07-24 | |
dc.identifier.citation | [1] J. G. Allen, R. Y. D. Xu, J. S. Jin, “Object Tracking Using CAMSHIFT Algorithm and Multiple Quantized Feature Spaces,” Proc. Pan-Sydney Area Workshop on Visual Information Processing, pp. 3-7, 2003.
[2] G. Bebis, S. Uthiram, and M. Georgiopoulos, “Face Detection and Verification Using Genetic Search,” International Journal of Artificial Intelligence Tools, Vol. 9, No. 2, pp. 225-246, 2000 [3] G. R. Bradski, “Computer Vision Face Tracking for Use in a Perceptual User Interface,” Intel Technology Journal, 2nd Quarter, 1998. [4] J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol. 2, pp. 121-167, 1998. [5] B. Castaneda, J. C. Cockkburn, “Reduce Support Vector Machines Applied to Real-Time Face Tracking,” ICASSP’05, Vol. 2, pp. 673-676, 2005. [6] C. C. Chang, C. J. Lin, “LIBSVM: A Library for Support Vector Machines,” http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001. [7] T. C. Chang, T. S. Huang, and C. Novak, “Facial Feature Extraction from Color Image,” Proceedings of the 12th International Conference on Pattern Recognition, Vol. 2, pp. 39-43, 1994. [8] R. Chellappa, C. L. Wilson, and S. Sirohey. “Human and Machine Recognition of Faces: A Survey,” Proceedings of IEEE, pp. 705-741, 1995. [9] Y. Cheng, “Mean Shift, Mode Seeking, and Clustering,” IEEE Transactions on Pattern Analysis Machine Intelligence, Vol. 17, pp. 790-799, 1995. [10] Y. T. Chung, “Face Tracking and Recognition,” Master Thesis, Department of Mechanical Engineering, National Taiwan University, 2004. [11] C. Corts and V. N. Vapnik, “Support Vector Networks,” Machine Learning, Vol. 20, pp. 273-297, 1995. [12] K. Crammer and Y. Singer, “On the Algorithmic Implementation of Multiclass Kernel-Based Vector Machines,” Technical Report, School of Computer Science and Engineering, Hebrew University, 2001. [13] R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2 Ed., Canada, 2001. [14] E. Durucan, T. Dbrahimi, “Change Detection and Background Extraction by Linear Algebra,” Proceedings of the IEEE, Vol. 89, No. 10, pp. 1368~1381, 2001. [15] M. D. Fairchild, “Color Appearance Models”, Addison-Wesley, Reading, MA. ISBN 0-201-63464-3, 1998. [16] Y. Fang, Y. Wang, T. Tan, “Combining Color, Contour and Region For Face Detection,” The 5th Asian Conference on Computer Vision, Melbourne, Australia, pp. 23-25, 2002. [17] R. Feraud, O. J. Bernier, J-E. Viallet, and M. Collobert, “A Fast and Accurate Face Detector Based on Neural Networks,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 23, pp. 42-53, 2001. [18] W. T. Freeman, K. Tanaka, J. Ohta, and K. Kyuma, “Computer Vision for Computer Games,” Int. Conf. On Automatic Face and Gesture Recognition, pp. 100-105, 1996. [19] K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd Edition. Academic Press, New York, 1990. [20] D. M. Gavrila, “The Visual Analysis of Human Movement: A Survey,” Computer Vision and Image Understanding, Vol. 75, No. 1, 1999. [21] R. C. Gonzalez, R. E. Woods, Digital Image Processing, 2 Ed., 2002. [22] G. Guo, S. Z. Li, and K. Chan. “Face Recognition by Support Vector Machines,” ICAFGR, pp.196-201, 2000. [23] I. Haritaoglu, D. Harwood, “Real-time Surveillance of People and Their Activites,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 809~829, 2000. [24] S. Haykin, Neural Networks: A Comprehensive Foundation, 2 Ed., Prentice-Hall, 1999. [25] B. Heisele and P. Ho and T. Poggio, “Face Recognition with Support Vector Machines Global Versus Component-Based Approach”, ICCV 2001, Vancouver. [26] E. Hjelmas and B. K. Low, “Face Detection: A Survey,” Computer Vision and Image Understanding, Vol. 83(2), pp. 236-274, 2001. [27] K. Hotta, “View-Invariant Face Detection Method Baded on Local PCA Cells,” Proceedings of the 12th International Conference on Image Analysis and Processing, 2003. [28] C. W. Hsu and C. J. Lin, “A Comparison of Methods for Multi-Class Support Vector Machines,” IEEE Transactions on Neural Networks. Vol. 13, No. 2, pp. 415-425, 2002. [29] C. W. Hsu, C. J. Lin and C. C. Chang, “A Practical Guide to Support Vector Classification,” Department of Computer Science and Information Engineering, National Taiwan University, Taiwan. [30] K. S. Huang and M. M. Trivedi, “Robust Real-Time Detection, Tracking, and Pose Estimation of Faces in Video Streams,” Proceedings of the 17th International Conference on Pattern Recognition, 2004. [31] F. van Dam and F. Hughes, Computer Graphics: Principles and Practice, 2 Ed.. [32] R. W. G. Hunt, “The Reproduction of Colour in Photography, Printing & Television”, 5th Ed. Fountain Press, England, 1995. [33] M. Isard and A. Blake. “Condensation-Conditional Density Propagation for Visual Tracking,” Int’l J. Computer Vision, Vol. 29(1) pp. 5-28, 1998. [34] G. Iyengar, C. Neti, “Detection of Faces Under Shadows and Lighting Variations”, IBM T. J. Watson Research Center. [35] D. S. Jang and H. I. Choi, “Active Models for Tracking Moving Objects,” Pattern Recognition, pp. 1135-1146, 2000. [36] K. Jung, K.I. Kim, T. Kurata, M. Kourogi, J. Han, “Text Scanner with Text Detection Technology on Image Sequences,” In Proc. International Conference on Pattern Recognition in Quebec City, Canada, Vol. 3, pp. 473-476, 2002. [37] C. H. Lee, S. W. Park, W. Chang, and J. W. Park, “Improving the Performance of Multi-Class SVMs in Face Recognition with Nearest Neighbor Rule,” Proceedings of the 15th IEEE ICTAI’03 2003. [38] Y. Li, S. Gong, and H. Liddell. “Support Vector Regression and Classification Based Multi-View Face Detection and Recognition,” In Automatic Face and Gesture Recognition, IEEE International Conference on, pp. 300-305, 2000. [39] J. S. Liu and R. Chen, “Sequential Monte Carlo Methods for Dynamic Systems,” Journal of American Statistical Association, on. Vol. 443, pp. 1032-1044, 1998. [40] B. Martinkauppi, “Face Colour under Varying Illumination - Analysis and Applications”, Department of Electrical and Information Engineering, University of Oulu Infotech Oulu, University of Oulu, ISBN 951-42-6788-5, 2002. [41] Y. Ming, J. Jiang and J. Ming, “Background Modeling and Subtraction Using a Local- Linear-Dependence-Based Cauchy Statistical Model,” Proceedings of the 7th Digital Image Computing: Techniques and Applications, pp. 10-12, 2003.. [42] D. J. Niu, Y. Z. Zhan, and S. L. Song, “Research and Implementation of Real-Time Face Detection, Tracking and Protection,” Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi’an, pp. 2-5, 2003. [43] R. Nowak, B. Narayan, “Probably Approximately Correct (PAC) Learning”, E901 Statistical Regularization and Learning Theory, 2004. [44] E. Osuna, R. Freund, F. Girosi, “Training Support Vector Machines: an Application to Face Detection.” Proceedings of Computer Vision and Pattern Recognition, 1997. [45] T. Phiasai, S. Arunrungrusmi, and K. Chamnongthai, “Face Recognition System with PCA and Moment Invariant Method,” Vol. 2, pp.165, IEEE, 2001. [46] J. C. Platt, N. Cristianini, and J. Shawe-Taylor, “Large Margin DAG’s for Multiclass Classification,” Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, Vol. 12, pp. 547-553, 2000. [47] H. Sahbi and N. Boujemaa. “Coarse-to-Fine Support Vector Classifiers for Face Detection,” ICPR, 3: 359-362, 2002. [48] W. S. Sarle, Neural Network FAQ. Periodic posting to the Usenet newsgroup comp.ai.neira;-nets. [49] A. Sato, A. Inoue, T. Suzuki, and T. Hosoi, “NeoFace-Development of Face Detection and Recognition Engine,” Multimedia Research Laboratories, Received, 2003. [50] H. Schneiderman and T. Kanade, “A statistical method for 3d object detection applied to faces and cars,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 746-751, 2000. [51] K. Schwerdt, J. L. Crowley, J.-B. Durand, “Robustification of Detection and Tracking of Faces,” Project PRIMA, Lab. [52] Y. B. Shalom, X. R. Li, T. Kirubarajan, Estimation with Applications to Tracking and Navigation, John Wiley and Sons, 2001. [53] Y. Shyrai, T. Yamane, and R. Okada, “Robust Visual Tracking by Integrating Various Cues,” IEICE, pp.951-958, 1998. [54] M. A. Turk, “Interactive-Time Vision: Face Recognition as a Visual Behavior,” Massachusetts Institute of Technology, 1991. [55] M. A. Turk and A. P. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Nenroscience, Vol. 3, pp.71-86, 1991. [56] V. N. Vapnik, “Statistical Learning Theory”, New York: John Wiley & Sons, 1998. [57] R. C. Verma, C. Schmid, and K. Mikolajcayk. Face Detection and Tracking in a Video by Propagating Detection Probabilities. IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 10, pp. 1216-1228, 2003. [58] V. Vezhnevets, V. Sazonov, and A. Andreeva, ”A Survey on Pixel-Based Skin Color Detection Techniques,” Graphics and Media Laboratory. [59] P. Wang and Q. Ji, “Multi-View Face Detection Under Complex Scene Based on Combined SVMs,” International Conference on Pattern Recognition, Vol. 4, pp. 179-182, 2004. [60] Y. Wang and B. Yuan, “Fast Method for Face Location and Tracking by Distributed Behaviour-Based Agents,” IEE Proceedings Image Signal Process. Vol. 149. No. 3., 2002. [61] M. H. Yan, D. Kriegman, and N. Ahuja, “Detecting faces in images: A survey,” IEEE Trans. Pattern Analysis and Machine Intelligence 24(1), pp. 34-58, 2002. [62] S.M. Yoon and H. Kim, “Real-Time Multiple People Detection Using Skin Color, Motion and Appearance Information,” Proceedings of the 2004 IEEE International Workshop on Robot and Human Interactive Communication Kurashiki, Okayama Japan, pp. 20-22, 2004. [63] Y. Zhong, A. K. Jain, and M.P. Dubutsson-Jolly, “Object Tracking Using Deformable Templates,” IEEE Trans. Pattern Anal. Mach. Intell, 22, (5), pp. 544-549., 2000. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/24387 | - |
dc.description.abstract | 本文的主要目的為發展多物件影像追蹤與多角度人臉偵測與辨識系統。我們提出Multi-CAMSHIFT來實現多物件追蹤,利用所感興趣的機率分布特性,例如:顏色、形狀,快速追蹤出物件輪廓以作為候選區域。整個系統架構於多解析度機制之上,可以有效改善系統效能並且降低龐大的運算量。配合多組主成分分析(PCA)和支持向量機(SVM),利用不同角度的奇異臉分析,結合成多角度人臉偵測與辨識模組,對於不同人臉姿勢加以分類與身分辨識。
我們的系統可應用於複雜背景以及即時追蹤,並且利用機率模型的更新機制,有效解決緩慢光源的變化。我們將上述的方法以及理論,成功地實現多角度人臉追蹤與辨識,並且應用於監測系統、人形追蹤和人臉辨識門禁等各系統。 | zh_TW |
dc.description.abstract | This thesis, aims to develop a system for multiple objects tracking and multi-view faces detection and recognition. We propose a novel method (Multi-CAMSHIFT), which is based on the characteristics of color and shape probability distribution, to solve the tracking problems for multiple objects. The tracker is used to get the candidate regions by outlining the interested probability distribution. The system performance is further improved by using multi-resolution framework and computation reduction. The principal component analysis (PCA) and support vector machine (SVM) are integrated to form the multi-view faces detection and recognition module for classifying different face poses and identities. Beside color information, the gray background image is used to locate the human head in the region of tracking pedestrian based on probability distribution rule. The rule can also be used for skin color face tracking to remove background region (non-face region).
Since the proposed Multi-CAMSHIFT (MCAMSHIFT) is computationally efficient, it can work in complex background and track in real-time. The slowly changing lighting condition is effectively resolved using probability model update. From experiments, the proposed MCAMSHIFT was successfully applied to multi-view faces tracking and recognition. It can also be applied to surveillance system, pedestrian tracking and face guard systems. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T05:24:08Z (GMT). No. of bitstreams: 1 ntu-94-R92522819-1.pdf: 3960812 bytes, checksum: d0ef57a84483ca0ce4fd76a86585795e (MD5) Previous issue date: 2005 | en |
dc.description.tableofcontents | 摘要 i
Abstract ii List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1. Motivation 1 1.2. Related Works 2 1.2.1. Object Tracking 2 1.2.2. Face Tracking and Recognition 4 1.3. Objectives and Contributions 6 1.4. Thesis Organization 9 Chapter 2 Background Knowledge 10 2.1. Color Space Used for Skin Modeling 10 2.2. The CAMSHIFT Algorithm 12 2.2.1. Introduction to the CAMSHIFT Algorithm 12 2.2.2. Mass Center Calculation 13 2.2.3. Probability Distribution 15 2.3. Principal Components Analysis (PCA) 16 2.4. Support Vector Machine (SVM) 20 2.4.1. Structural Risk Minimization 20 2.4.2. Introduction to SVMs 21 Chapter 3 Multiple Objects Tracking 27 3.1. Interested probability Modeling 27 3.1.1. Skin Color Probability Modeling 27 3.1.2. Background Probability Modeling 31 3.2. Probability Model Update 33 3.2.1. Adaptive Skin Color Probability Model Update 34 3.2.2. Adaptive Background Probability Model Update 34 3.3. Modified CAMSHIFT Algorithm 36 3.3.1. Interested probability Enhancement 38 3.3.2. Multi-Resolution Framework 40 3.3.3. Initial Block Searching in Small Resolution 42 3.3.4. Search Window of CAMSHIFT 44 3.3.5. Center Tendency 45 3.4. Multi-CAMSHIFT Algorithm (MCAMSHIFT) 47 3.4.1. Sort Indexes of MCAMSHIFT 50 Chapter 4 Multi-View Faces Detection and Recognition 52 4.1. Face Pattern Enhancement and Classification 52 4.1.1. Pattern Histogram Equalization Enhancement 53 4.1.2. Eigenfaces 55 4.1.3. Mask Filter 56 4.1.4. SVM Data Scaling 59 4.1.5. PCA and SVMs Face Classifier 60 4.2. Multi-View Faces Module 64 4.2.1. Multi-View Faces Representation 65 4.2.2. Combined PCA-SVMs Module 66 4.3. Multiple Faces Tracking and Recognition 70 4.3.1. PID Control Theorem in Pan-Tilt System 72 4.3.2. Moving Pedestrians Tracking and Heads Tracking 73 Chapter 5 Applications and Experimental Results 75 5.1. System Overview 75 5.2. Applications 77 5.2.1. Face Tracking and Recognition 79 5.2.2. Surveillance System and Guard System 81 5.3. Performance of MCAMSHIFT Tracking 84 5.4. Face Recognition Experiments 88 5.4.1. Static Multi-View Faces Recognition Experiments 89 5.4.2. Dynamic Multi-View Faces Recognition Experiments 90 Chapter 6 Conclusions 93 6.1. Conclusions 93 6.2. Future Works 94 References 95 | |
dc.language.iso | en | |
dc.title | Multi-CAMSHIFT應用於多角度人臉追蹤與辨識 | zh_TW |
dc.title | Multi-CAMSHIFT for Multi-View Faces Tracking and Recognition | en |
dc.type | Thesis | |
dc.date.schoolyear | 93-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 羅仁權,傅楸善 | |
dc.subject.keyword | 人臉,追蹤,偵測,辨識,多角度人臉,主成分分析,支持向量機, | zh_TW |
dc.subject.keyword | face,tracking,detection,recognition,multi-view,PCA,SVM, | en |
dc.relation.page | 100 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2005-07-25 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
顯示於系所單位: | 機械工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-94-1.pdf 目前未授權公開取用 | 3.87 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。