請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/36586完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳永耀 | |
| dc.contributor.author | Kun-Te Li | en |
| dc.contributor.author | 李冠德 | zh_TW |
| dc.date.accessioned | 2021-06-13T08:06:41Z | - |
| dc.date.available | 2006-07-28 | |
| dc.date.copyright | 2005-07-28 | |
| dc.date.issued | 2005 | |
| dc.date.submitted | 2005-07-21 | |
| dc.identifier.citation | [1]. T. Mori, K. Tsujioka and T. Sato, ”Human-like action recognition system on whole body motion-captured file”, Intelligent Robots and Systems, 2001. Proceedings. 2001 IEEE/RSJ International Conference on Volume 4, Page(s): 2066 - 2073 vol.4, 29 Oct.-3 Nov. 2001.
[2]. J.K. Aggarwal and Q. Cai, ”Human motion analysis: a review”, Nonrigid and Articulated Motion Workshop, 1997. Proceedings., IEEE, Page(s):90 – 102,16 June 1997. [3]. J. Yamato, J. Ohya and K. Ishii, ”Recognizing human action in time-sequential images using hidden Markov model”, Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92., 1992. IEEE Computer Society Conference, Page(s):379 – 385, 15-18 June 1992. [4]. Y. Isoda, S. Kurakake and H. Nakano,”Ubiquitous sensors based human behavior modeling and recognition using a spatio-temporal representation of user states”, Advanced Information Networking and Applications, 2004. AINA 2004. 18th International Conference on Volume 1, Page(s):512 - 517 Vol.1, 2004. [5]. G. Bebis, D. Egbert and M. Shah, ”Review of computer vision education”, Education, IEEE Transactions on Volume 46, Issue 1, Page(s):2 – 21, Feb. 2003. [6]. T. Mori, K. Tsujioka, M. Shimosaka and T. Sato, ”Human-like action recognition system using features extracted by human”, Intelligent Robots and System, 2002. IEEE/RSJ International Conference on Volume 2, 30 Sept.-5 Oct. Page(s):1214 - 1220 vol.2. 2002. [7]. http://www.bvhfiles.com/ [8]. T. Mori, Y. Kamisuwa, H. Mizoguchi and T. Sato, ”Action Recognition System Based on Human Finder and Human Tracker”, Intelligent Robots and Systems, 1997. [9]. G. Johansson, “Visual perception of biological motion and a model for its analysis”, Perception Psychophysics, 14(2)X, 201-211, 1997. [10]. C. Ceras and M. Shah, “Motion-Based Recognition: A survey”, Image and Vision Computation, 13(2):129-155, March 1995. [11]. A. Wilson and A. Bobick, “Parameter Hidden Markov Models for Gesture Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(9), 1999. [12]. C. Wren and A. Pentland, “Dynamic Models of Human Motion”, In the third International Conference on Automatic Face and Gesture Recognition, pages 22-27, IEEE, 1998. [13]. J.K. Aggarwal, Qin Cai, W. Liao and B. Sabata, ”Nonrigid motion analysis: Articulated and elastic motion”, Computer Vision and Image Understanding, pages 142~156, 1997. [14]. D. Ayers and M. Shah, “Recognizing human action in a static room”, In Proceedings Computer Vision and Pattern Recognition, pages 42-46, 1998. [15]. Stephen S. Intille, J. Davis and A. Bobick, ”Real time closed world tracking”, In Proceedings IEEE International Conference on Computer Vision and Pattern Recognition, pages 697~703, 1997. [16]. James Davis and Aaron Bobick, “The representation and recognition of action using temporal plates”, In Proceedings Computer Vision and Pattern Recognition, pages 928-934, 1997. [17]. Nuria Oliver Barbara Rosario and Alex Pentland, “A bayesian computer vision system for modeling human interactions”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, August, 2000. [18]. Jie Yang, Yangsheng Xu, and Chiou S. Chen, “Human action learning via hidden markov model”, IEEE Transactions on Systems, Man and Cybernetics, A:34-44, 1997. [19]. http://www.credo-interactive.com/products/index.html [20]. http://www.centralsource.com/blender/bvh/what.htm [21]. http://www.metamotion.com/ [22]. L. A. Zadeh, “Fuzzy sets”, Information and Control, 8, 338-353, 1965. [23]. E. H. Mamdani and S. Asslilan, “A fuzzy logic controller for a dynamic plant”, International Journal of Man-Machine Studies, Vol.7, PP.1-13, 1975. [24]. Chin-Teng Lin, C. S. George Leem “Neural Fuzzy Systems”, Prentice Hall International, 1999, [25]. Hao Ying, “Fuzzy Control and Modeling”, Institute of Electrical and Electronics Engineers, 2000. [26]. J.K. Aggarwal and Sangho Park, “Human Motion: Modeling and Recognition of Actions and Interactions”, 3DPVT04(640-647). IEEE Abstract. IEEE Top Reference. 0412 BibRef, 2004. [27]. C. Kambhamettu, D. Goldgof, D. Terzopoulos and T. Huang, “Nonrigid motion analysis”, Handbook of PRIP: Computer Vision, 2, 1994. [28]. C. R. Wren, A. Azarbayejani, T. Darrell and A. P. Pentland, “Pfinder: real-time tracking of the human body”, Trans. Pattern Anal. Mach. Intelligence 19(7), 780–785, 1997. [29]. T. B. Moeslund and E. Granum, “Multiple cues used in model-based human motion capture”, in The Fourth International Conference on Automatic Face and Gesture Recognition, Grenoble, France, March 2000. [30]. T. Darrell, P. Maes, B. Blumberg and A. P. Pentland, “A novel environment for situated vision and behavior”, in Workshop for Visual Behaviors at CVPR-94, 1994. [31]. A. Nakazawa, H. Kato and S. Inokuchi, “Human tracking using distributed video systems”, in International Conference on Pattern Recognition, 1998. [32]. M. Brand, “Shadow puppetry”, in International Conference on Computer Vision, Corfu, Greece, September 1999. [33]. R. Rosales and S. Sclaroff, “Learning and synthesizing human body motion and posture”, in The Fourth International Conference on Automatic Face and Gesture Recognition, Grenoble, France, March 2000. [34]. D. Gavrila and L. Davis, “3-d model-based tracking of human upper body movement: a multi-view approach”, In Proceedings of Int’l Symposium on Computer Vision, pages 253– 258, 1995. [35]. M. Isard and A. Blake, “Condensation - conditional density propagation for visual tracking”, Int. Journal of Computer Vision, 29(1):5–28, 1998. [36]. H. J. Lee and Z. Chen, “Knowledge-guided visual perception of 3-D human gait from a single image sequence”, Trans. Systems, Man, Cybernetics 22(2), 336–342, 1992. [37]. M. K. Leung and Y. H. Yang, “First sight: A human body outline labeling system”, Trans. Pattern Anal. Mach. Intelligence 17(4), 359–377, 1995. [38]. D. Hogg, “Model-based vision: A program to see a walking person”, Image and Vision Computing 1(1), 5–20, 1983. [39]. R. Jain and H. H. Nagel, “On the analysis of accumulative difference pictures from image sequences of real scenes”, IEEE Trans. on PAMI, 1(2):206-214, 1979. [40]. D. Marr and H. K. Nishihara, “Representation and recognition of the spatial organization of three dimensional shapes”, In Proc. R. Soc. London, volume B, pages 269-394, 1978. [41]. K. Rohr, “Human Movement Analysis Based on Explicit Motion Models”, chap. 8, pp. 171–198, Kluwer Academic, Dordrecht/Boston, 1997. [42]. J. Farmer, M. Casdagli, S. Eubank and J. Gibson, “State-space reconstruction in the presence of noise”, Physics D, 51D:52-98, 1991. [43]. J. Yamato, J. Ohya and K. Ishii, “Recognizing human action in time-sequential images using Hidden Markov Model”, In Proc. IEEE Conf. CVPR, pages 379-385, Champaign, IL, June 1992. [44]. A. F. Bobick and A. D. Wilson. A, ”State-based technique for the summarization and recognition of gesture”, In Proc. of 5th Intl. Con) on Computer Vision, pages 382-388, 1995. [45]. A. F. Bobick and J. Davis, “Real-time recognition of activity using temporal templates”, In Pmc. of IEEE Computer Society Workshop Applications on Computer Vision, pages 39-42, Sarasota, FL, 1996. [46]. R. Polana and R Nelson, “Low level recognition of human motion (or how to get your man without finding his body parts)”, In Pmc. of lEEE Computer Society Workshop on Motion of Non-Rigid and Articulated Objects, pages 77-82, Austin, TX, 1994. [47]. 張意政, ” Non-rigid Motion Analysis for Human Body ”, 國立清華大學電機工程研究所博士論文, 1999. [48]. C. Becchetti and L. P. Ricotti, “Speech Recognition Theory and C++ Implementation”, New York:Wiley, 1999. [49]. L. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, Proceeding of the IEEE, vol. 77, No. 2, February 1989. [50]. L. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition”, Proceedings of the IEEE, 77(2):257 –286, 1989. [51]. M. Shah and R. Jain, editors, “Motion-Based Recognition”, chapter 9, pages 201–226. Kluwer Academic Publishers, 1997. [52]. H. S. J. O. T. Min, B. Yoon and T. Ejima, “Visual recognition of static/dynamic gesture: Gesture-driven editing system”, Journal of Visual Languages and Computing, 10:291–309, 1999. [53]. J. Yang, Y. Xu, and C. S. Chen, “Human action learning via hidden markov model”, IEEE Transactions on Systems, Man and Cybernetics, pages 34–44, 1997. [54]. M. Herman, “Understanding body postures of human stick figures”, PhD thesis, University of Maryland, 1979. [55]. K. Akita, ”Image sequence analysis of real world human motion”, Pattern Recognition, 17 (1), 1984. [56]. R. Mann, A. Jepson and J. Siskind, “Computational perception of scene dynamics”, Computer Vision and Image Understanding,65 (2):113–128, 1997. [57]. T. H. Cormen, C. E. Leiserson and R. L. Rivest, “Introduction to Algorithms”, MIT Press, 1998. [58]. S. Intille and A. Bobick, “Representation and visual recognition of complex, multi-agent actions using belief networks”, Technical Report No. 454, MIT, 1998. [59]. M. Brand and I. Essa, “Causal analysis for visual gesture understanding”. MIT Tech. Report, 1995. [60]. Y. Ivanov and A. Bobick, “Recognition of visual activities and interactions by stochastic parsing”, IEEE transactions on Pattern Analysis and Machine Intelligence, 22(8):852–872, 2000. [61]. D. Ayers and M. Shah, “Recognizing human action in a static room”, In Proceedings of IEEE Computer Society Workshop on Interpretation of Visual Motion, pages 42–46, 1998. [62]. T. Wada and T. Matsuyama, “Multiobject behavior recognition by event driven selective attention method”, IEEE transaction on Pattern Analysis and Machine Intelligence, 22(8):873–887, August 2000. [63]. S. Park and J.K. Aggarwal, “Event semantics in two-person interactions”, In International Conference on Pattern Recognition, Cambridge, UK, 2004. [64]. 徐仲安, ”Extraction of Parametric Human Posture Model Using Genetic Algorithm”, 國立台灣大學電機工程研究所碩士論文, 2004. [65]. 王文俊, “認識Fuzzy”, 全華圖書, 2001. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/36586 | - |
| dc.description.abstract | 辨識人體運動行為是現今電腦視覺中一項有趣的研究主題，已被廣泛地運用在虛擬實境、監控系統、人機介面、人體動作分析與多媒體壓縮等應用上。在含人體運動的連續畫面中，辨識人體運動行為主要可分兩大部份:人體資訊特徵的取得及人體動作行為的解析表達。人體資訊特徵取得的困難在於如何從影像中找出人體部份，而人體動作行為解析表達的挑戰在於如何利用人體資訊特徵辨識多變複雜的人體動作。
本篇論文針對人體動作行為解析表達這方面，提出一套針對居家看護的人體動作辨識系統。其做法是依據人類辨識動作的直觀判斷，設定每個動作之規則條件，再藉由判斷人體資訊特徵是否符合動作條件而辨識之，且為了居家看護的需求，特別要求跌倒等危險動作的判斷。本系統利用3D人體動畫軟體取代影像的人體資訊特徵取得，藉由模糊理論比較人體資訊與動作規則之間的符合程度，以符合程度最高的動作為系統之輸出。實驗中將利用3D人體動畫與程式輸出的比對，證明本方法可以成功的辨識解析人體動作。 | zh_TW |
| dc.description.abstract | Recognition of human activities has become an interesting research topic in computer vision, with a wide range of applications in virtual reality, surveillance systems, human-computer interface, human motion analysis and multimedia compression, etc. Recognition of human activities mainly involves two aspects: acquirement of human motion information and representation of human activities. The difficulties of human information acquirement are to extract human body information and characteristics from the image sequence. The challenge of human activities representation is how to utilize human information to recognize complex and varied human actions.
This thesis focuses on the representation of human activities. A human action recognition system for home-care is presented. The methodology of recognition system depends on the intuitional identification of human to design individual rules for each action. According to the correlation between rules and human information, actions can be recognized. This system extracts the human body information from the image sequence which is generated by a 3D human body animation software. Fuzzy set theory is utilized to derive the weighting of each action. By comparing the weighting of each action, the action with the largest weighting is chosen as the output of this recognition system. According to a series of experiments, the comparison between 3D human animation and program results shows that the proposed system can recognize human actions successfully. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T08:06:41Z (GMT). No. of bitstreams: 1 ntu-94-R92921012-1.pdf: 1214318 bytes, checksum: e1c29496386727ca48b62270dc8fddc7 (MD5) Previous issue date: 2005 | en |
| dc.description.tableofcontents | Abstract i
摘要 ii Contents iii List of Figures v List of Tables vii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Problem Definition 2 1.3 Previous Methods 3 1.4 Approach 5 1.5 Thesis Overview 7 Chapter 2 Human Activities Identification 9 2.1 Human Body Modeling 10 2.1.1 View-Based Approaches 11 2.1.2 Model-Based Approaches 12 2.2 Human Activities Recognition Approaches 16 2.2.1 Human Action Recognition Approaches 17 2.2.2 Behavior Recognition Schemes with Domain Knowledge 23 2.3 Summary 26 Chapter 3 Design of Human Action Recognition 29 3.1 Characteristics of Action Identification by Human 30 3.1.1 Recognition by Features of Actions 30 3.1.2 Multiple Features for One Action 31 3.1.3 Fuzziness in Action Identification 31 3.2 Classification of Human Actions 32 3.3 Fuzzy Set Theory 35 3.3.1 Introduction to Fuzzy Set Theory 36 3.3.2 Fuzzy Set 37 3.3.3 Membership Function 39 3.3.4 Fuzzy Rule 40 3.3.5 Fuzzy Inference 41 3.4 Fuzzy Action Recognition 42 3.4.1 Fuzzification 43 3.4.2 Rule Base 44 3.4.3 Fuzzy Inference Engine 45 3.5 Rule Design 48 3.6 Summary 50 Chapter 4 Implementation of Human Action Recognition System 51 4.1 System Architecture 51 4.2 Front-End Simulation 54 4.2.1 The Structure of Human Model 55 4.2.2 Biovision Hierarchical File 58 4.3 Recognition Factors Derivation 59 4.4 Summary 63 Chapter 5 Simulation and Results 65 5.1 Basic Test 66 5.2 Special Test for Home Care System 70 5.3 Composite Test 73 5.4 Summary 75 Chapter 6 Conclusion 77 References 79 | |
| dc.language.iso | en | |
| dc.subject | 模糊理論 | zh_TW |
| dc.subject | 人體動作辨識 | zh_TW |
| dc.subject | 居家看護 | zh_TW |
| dc.subject | human action recognition | en |
| dc.subject | home-care system | en |
| dc.subject | fuzzy set theory | en |
| dc.title | 應用於居家看護系統之人體動作辨識模糊規則設計 | zh_TW |
| dc.title | Fuzzy Rule-Based Human Actions Recognition for Home Care System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 93-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林進燈,顏家鈺 | |
| dc.subject.keyword | 人體動作辨識,模糊理論,居家看護, | zh_TW |
| dc.subject.keyword | human action recognition,fuzzy set theory,home-care system, | en |
| dc.relation.page | 85 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2005-07-21 | |
| 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 |
| 顯示於系所單位： | 電機工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-94-1.pdf 未授權公開取用 | 1.19 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。