Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19627
Full metadata record
???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
---|---|---|
dc.contributor.advisor | 許永貞 | |
dc.contributor.author | Shih-Han Wang | en |
dc.contributor.author | 王詩翰 | zh_TW |
dc.date.accessioned | 2021-06-08T02:09:31Z | - |
dc.date.copyright | 2016-02-16 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2016-01-28 | |
dc.identifier.citation | Bibliography
[1] Wongun Choi, Khuram Shahid, and Silvio Savarese. Learning context for collective activity recognition. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 3273–3280. IEEE, 2011. [2] Akin Avci, Stephan Bosch, Mihai Marin-Perianu, Raluca Marin-Perianu, and Paul Havinga. Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. In Architecture of computing systems (ARCS), 2010 23rd international conference on, pages 1–10. VDE, 2010. [3] Daniel H Wilson and Chris Atkeson. Simultaneous tracking and activity recognition (star) using many anonymous, binary sensors. In Pervasive computing, pages 62–79. Springer, 2005. [4] Chun Zhu and Weihua Sheng. Wearable sensor-based hand gesture and daily activity recognition for robot-assisted living. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 41(3):569–573, 2011. [5] Jie Yin, Qiang Yang, and Jeffrey Junfeng Pan. Sensor-based abnormal humanactivity detection. Knowledge and Data Engineering, IEEE Transactions on, 20(8): 1082–1090, 2008. [6] Qiang Yang. Activity recognition: Linking low-level sensors to high-level intelligence. In IJCAI, volume 9, pages 20–25, 2009. [7] Vikramaditya R Jakkula and Diane J Cook. Enhancing smart home algorithms using temporal relations. Technology and Aging, 21:3–10, 2008. [8] Tao Gu, Shaxun Chen, Xianping Tao, and Jian Lu. An unsupervised approach to activity recognition and segmentation based on object-use fingerprints. Data & Knowledge Engineering, 69(6):533–544, 2010. [9] Parisa Rashidi, Diane J Cook, Lawrence B Holder, and Maureen Schmitter- Edgecombe. Discovering activities to recognize and track in a smart environment. Knowledge and Data Engineering, IEEE Transactions on, 23(4):527–539, 2011. [10] Diane J Cook, Narayanan C Krishnan, and Parisa Rashidi. Activity discovery and activity recognition: A new partnership. Cybernetics, IEEE Transactions on, 43(3): 820–828, 2013. [11] P. Rashidi G. Acampora, D. Cook and A. Vasilakos. Healthcare data analystics. Data Analytics for Healthcare, 2015. [12] Parisa Rashidi and Diane J Cook. Keeping the resident in the loop: Adapting the smart home to the user. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 39(5):949–959, 2009. [13] Chao Chen, Barnan Das, and Diane J Cook. A data mining framework for activity recognition in smart environments. In Intelligent Environments, pages 80–83, 2010. [14] Diane J Cook and Narayanan Krishnan. Mining the home environment. Journal of intelligent information systems, 43(3):503–519, 2014. [15] Aaron Crandall and Diane J Cook. Learning activity models for multiple agents in a smart space. In Handbook of Ambient Intelligence and Smart Environments, pages 751–769. Springer, 2010. [16] Liming Chen, Jesse Hoey, Chris D Nugent, Diane J Cook, and Zhiwen Yu. Sensorbased activity recognition. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 42(6):790–808, 2012. [17] Diane J Cook. Learning setting-generalized activity models for smart spaces. IEEE intelligent systems, 2010(99):1, 2010. [18] Feng-Tso Sun, Yi-Ting Yeh, Heng-Tze Cheng, Cynthia Kuo, and Martin Griss. Nonparametric discovery of human routines from sensor data. In Pervasive Computing and Communications (PerCom), 2014 IEEE International Conference on, pages 11–19. IEEE, 2014. [19] Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, and Michael L Littman. Activity recognition from accelerometer data. In AAAI, volume 5, pages 1541–1546, 2005. [20] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge L Reyes- Ortiz. Human activity recognition on smartphones using a multiclass hardwarefriendly support vector machine. In Ambient assisted living and home care, pages 216–223. Springer, 2012. [21] Huimin Qian, Yaobin Mao, Wenbo Xiang, and Zhiquan Wang. Recognition of human activities using svm multi-class classifier. Pattern Recognition Letters, 31(2):100– 111, 2010. [22] Samy Sadek, Ayoub Al-Hamadi, and Bernd Michaelis. Toward real-world activity recognition: An svm based system using fuzzy directional features. WSEAS Transactions on Information Science & Applications, 10(4), 2013. [23] Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. Liblinear: A library for large linear classification. The Journal of Machine Learning Research, 9:1871–1874, 2008. [24] Dirk Schulz, Dieter Fox, and Jeffrey Hightower. People tracking with anonymous and id-sensors using rao-blackwellised particle filters. In IJCAI, pages 921–928, 2003. [25] Christian Wojek, Kai Nickel, and Rainer Stiefelhagen. Activity recognition and room-level tracking in an office environment. In Multisensor Fusion and Integration for Intelligent Systems, 2006 IEEE International Conference on, pages 25–30. IEEE, 2006. [26] Michael Jordan. Stat 260/CS 294, Bayesian Modeling and Inference, 2010. [27] Anirban DasGupta. Probability for statistics and machine learning: fundamentals and advanced topics. Springer Science & Business Media, 2011. [28] Thomas S Ferguson. A bayesian analysis of some nonparametric problems. The annals of statistics, pages 209–230, 1973. [29] Amol Kapila Bela A. Frigyik and Maya R. Gupta. Introduction to the dirichlet distribution and related processes. Technical report, Department of Electrical Engineering, University of Washington, December 2010. [30] David Blackwell and James B MacQueen. Ferguson distributions via pólya urn schemes. The annals of statistics, pages 353–355, 1973. [31] David J Aldous. Exchangeability and related topics. Springer, 1985. [32] Jayaram Sethuraman. A constructive definition of dirichlet priors. Technical report, DTIC Document, 1991. [33] Jim Pitman. Poisson–dirichlet and gem invariant distributions for split-and-merge transformations of an interval partition. Combinatorics, Probability & Computing, 11(05):501–514, 2002. [34] Yee Whye Teh, Michael I Jordan, Matthew J Beal, and David M Blei. Hierarchical dirichlet processes. Journal of the american statistical association, 101(476), 2006. [35] Chong Wang and David M Blei. A split-merge mcmc algorithm for the hierarchical dirichlet process. arXiv preprint arXiv:1201.1657, 2012. [36] Chong Wang. Hdp library. http://www.cs.princeton.edu/~chongw/ resource.html, 2010. [37] Charles E Antoniak. Mixtures of dirichlet processes with applications to bayesian nonparametric problems. The annals of statistics, pages 1152–1174, 1974. [38] Bernhard E Boser, Isabelle M Guyon, and Vladimir N Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, pages 144–152. ACM, 1992. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19627 | - |
dc.description.abstract | 在這個簡單異質環境感測器的時代,行為辨識在各領域扮演重要的角色,例如辨識行為與偵測異常。但是,大多數的研究只針對其中一種,所以我們認為一個廣義的方法應該從挖掘數據的高層描述開始,這樣我們的方法就可以適用於各種模型,而有不同的用途。本論文中,我們提出非參數主題模型的架構進而從五個月的簡單異質環境感測器資料中萃取高層次的資料樣式,我們的方法可以被用在房間中並考量各種環境感測器資料。我們有兩種結果,在質性結果中,我們將資料樣式視覺化,讓人可以搭配一些照片驗證就能夠看出異常的活動,所以我們成功地在這些資料中找出曾經上過新聞的異常人士;在我們的量化分析中,結果顯示我們的方法可以有效的壓縮資料,讓空間節省超過 99% ,平均而言還可以達到最好的辨識率超過87%準確率,在偵測異常中,我們的方法可以有效地呈現高層次資料,讓支持向量機可以偵測出異常。總結來說,我們的方法可以有效呈現高層次資料樣式,且被用來解決真實世界的異常活動問題。 | zh_TW |
dc.description.abstract | In the era of simple-heterogeneous-environmental(SHE) sensors, activity recognition plays an important role in many applications, such as recognizing normal activity and detecting abnormal activity. However, most research was conducted for either one but lost generality to both. We, therefore, start by developing higher-level representations for any qualified model to perform prediction as apposed to designing a specific model to fit certain data. In this thesis, we proposed a framework by non-parametric topic modeling to extract pattern as a higher-level representation from 5-month SHE sensor data. Our method can be used in a room with multiple sensors deployed and allow sensor fusion. In experiment, we have qualitative results and quantitative results. In our qualitative results, we generated visualized patterns for humans to detect abnormal activity. As a result, we successfully detect an intruder who was once reported on the news. In the quantitative evaluation, we demonstrate that the proposed framework can significantly compress sensor data over 99% and achieve best performance over 87% of accuracy on average in activity recognition. In anomaly detection, the proposed framework can effectively extract descriptive patterns for one-class SVM to detect abnormal activity. To summarize, we have shown the effective results of the proposed framework and use it to solve a real world problem, anomaly detection in a room. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T02:09:31Z (GMT). No. of bitstreams: 1 ntu-104-R02944022-1.pdf: 5957281 bytes, checksum: a78b9bea7b93b25ed2d7f99de93dd2b9 (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | Contents
口試委員會審定書 i 致謝ii Abstract iii 中文摘要iv Contents v List of Figures viii List of Tables x 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 An Intelligent Building Block . . . . . . . . . . . . . . . . . . . 2 1.1.2 Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Background 5 2.1 Temporal Relationship of Activities . . . . . . . . . . . . . . . . . . . . 5 2.2 Non-intrusive Sensor Network and Heterogeneous Sensors . . . . . . . . 6 2.3 Feature Extraction and Feature Selection . . . . . . . . . . . . . . . . . . 7 2.4 Machine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . 8 2.4.1 Topic Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.4.2 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . 11 2.5 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3 Problem Formulation and Framework 13 3.1 Problem Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.2 Observation and Hypothesis . . . . . . . . . . . . . . . . . . . . 14 3.1.3 Exchangeability . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.1 Pattern Extraction by Topic Modeling . . . . . . . . . . . . . . . 16 3.2.2 Activity Recognition . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.3 Anomaly Detection . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.3 The System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3.1 The Pattern Extraction Framework . . . . . . . . . . . . . . . . . 18 3.3.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4 Pattern Extraction 20 4.1 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2 Dimension Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.3 Word Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3.1 Discretization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3.2 Dirichlet Distribution . . . . . . . . . . . . . . . . . . . . . . . . 23 4.3.3 Dirichlet Process . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.3.4 Dirichlet Process Gaussian Mixture Models . . . . . . . . . . . . 30 4.4 Hidden Topic Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.4.1 Hierarchical Dirichlet Process . . . . . . . . . . . . . . . . . . . 32 4.4.2 Inference of Hierarchical Dirichlet Process . . . . . . . . . . . . 36 5 Applications 39 5.1 Activity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.1.1 Linear Support Vector Machine . . . . . . . . . . . . . . . . . . 39 5.2 Anomaly Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.2.1 One-class Support Vector Machine . . . . . . . . . . . . . . . . . 41 6 Experiment 42 6.1 Experimental Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 6.1.1 Sensing Platform . . . . . . . . . . . . . . . . . . . . . . . . . . 42 6.1.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 6.1.3 Activities in Room 324 and Time Window . . . . . . . . . . . . 44 6.1.4 Ground Truth . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.2 Pattern Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.2.1 Normal Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.2.2 Abnormal Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . 46 6.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 6.3.1 Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 6.3.2 Activity Recognition . . . . . . . . . . . . . . . . . . . . . . . . 54 6.3.3 Anomaly Detection . . . . . . . . . . . . . . . . . . . . . . . . . 57 7 Conclusion 60 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 A Notation Table 62 Bibliography 63 | |
dc.language.iso | en | |
dc.title | 非參數主題模型於具異常偵測之行為辨識技術的研究 | zh_TW |
dc.title | Activity Recognition with Anomaly Detection Using Non-Parametric Topic Modeling | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林守德,李育杰,蔡宗翰,吳兆麟 | |
dc.subject.keyword | 非參數主題模型,異常偵測,行為辨識, | zh_TW |
dc.subject.keyword | Non-parametric topic model,anomaly detection,activity recognition, | en |
dc.relation.page | 67 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2016-01-28 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
Appears in Collections: | 資訊網路與多媒體研究所 |
Files in This Item:
File | Size | Format | |
---|---|---|---|
ntu-104-1.pdf Restricted Access | 5.82 MB | Adobe PDF |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.