請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65193完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
| dc.contributor.author | Kuan-Ting Yu | en |
| dc.contributor.author | 俞冠廷 | zh_TW |
| dc.date.accessioned | 2021-06-16T23:29:18Z | - |
| dc.date.available | 2017-08-03 | |
| dc.date.copyright | 2012-08-03 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-07-30 | |
| dc.identifier.citation | [1] J. Civera, D. Galvez-Lopez, L. Riazuelo, J. D. Tardos, and J. M. M. Montiel, 'Towards semantic SLAM using a monocular camera,' in Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2011.
[2] N. Blodow, L. C. Goron, Z.-C. Marton, D. Pangercic, T. Ruhr, M. Tenorth, and M. Beetz, 'Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments,' in Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2011. [3] E. R. Nascimento, G. L. Oliveira, M. F. M. Campos, and A. W. Vieira, 'Improving Object Detection and Recognition for Semantic Mapping with an Extended Intensity and Shape based Descriptor,' in IROS 2011 workshop - Active Semantic Perception and Object Search in the Real World, 2011. [4] J. G. Rogers, A. J. B. Trevor, C. Nieto-Granda, and H. I. Christensen, 'Simultaneous localization and mapping with learned object recognition and semantic data association,' in Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2011. [5] A. Nuchter and J. Hertzberg, 'Towards semantic maps for mobile robots,' Robotics and Autonomous Systems, vol. 56, pp. 915-926, 2008. [6] D. Anguelov, R. Biswas, D. Koller, B. Limketkai, and S. Thrun, 'Learning hierarchical object maps of non-stationary environments with mobile robots,' in Proc. of the Eighteenth conference on Uncertainty in artificial intelligence, 2002. [7] R. B. Rusu, Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz, 'Towards 3D Point cloud based object maps for household environments,' Robotics and Autonomous Systems, vol. 56, pp. 927-941, 2008. [8] K. Lai, L. Bo, X. Ren, and D. Fox, 'Detection-based Object Labeling in 3D Scenes,' in Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2012. [9] H. S. Koppula, A. Anand, T. Joachims, and A. Saxena, 'Semantic labeling of 3d point clouds for indoor scenes,' in Neural Information Processing Systems, 2011. [10] X. Ren, L. Bo, and D. Fox, 'RGB-(D) Scene Labeling: Features and Algorithms,' in IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2012. [11] A. Rottmann, O. M. Mozos, C. Stachniss, and W. Burgard, 'Semantic place classification of indoor environments with mobile robots using boosting,' in National Conference on Artificial Intelligence, 2005. [12] O. Martinez Mozos, R. Triebel, P. Jensfelt, A. Rottmann, and W. Burgard, 'Supervised semantic labeling of places using information extracted from sensor data,' Robotics and Autonomous Systems, vol. 55, pp. 391-402, 2007. [13] H. Zender, O. Martinez Mozos, P. Jensfelt, G. J. M. Kruijff, and W. Burgard, 'Conceptual spatial representations for indoor mobile robots,' Robotics and Autonomous Systems, vol. 56, pp. 493-502, 2008. [14] C. Galindo, J.-A. Fernandez-Madrigal, J. Gonzalez, and A. Saffiotti, 'Robot task planning using semantic maps,' Robotics and Autonomous Systems, vol. 56, pp. 955-966, 2008. [15] S. Vasudevan, S. Gachter, V. Nguyen, and R. Siegwart, 'Cognitive maps for mobile robots—an object based approach,' Robotics and Autonomous Systems, vol. 55, pp. 359-371, 2007. [16] S. Vasudevan and R. Siegwart, 'Bayesian space conceptualization and place classification for semantic maps in mobile robotics,' Robotics and Autonomous Systems, vol. 56, pp. 522-537, 2008. [17] M. Pantic, A. Pentland, A. Nijholt, and T. Huang, 'Human Computing and Machine Understanding of Human Behavior: A Survey,' in Artifical Intelligence for Human Computing. vol. 4451, T. Huang, A. Nijholt, M. Pantic, and A. Pentland, Eds., ed: Springer Berlin / Heidelberg. [18] I. Laptev and G. Mori. (2010). Statistical and Structural Recognition of Human Actions [Online]. Available: https://sites.google.com/site/humanactionstutorialeccv10/ [19] Z. Zhihong, M. Pantic, G. I. Roisman, and T. S. Huang, 'A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, pp. 39-58, 2009. [20] R. B. Rusu, N. Blodow, and M. Beetz, 'Fast Point Feature Histograms (FPFH) for 3D registration,' in Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2009. [21] R. B. Rusu, G. Bradski, R. Thibaux, and J. Hsu, 'Fast 3D recognition and pose using the Viewpoint Feature Histogram,' in Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2010. [22] P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, 'RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments,' The International Journal of Robotics Research, vol. 31, pp. 647-663, 2012. [23] C.-C. Wang, C. Thorpe, S. Thrun, M. Hebert, and H. Durrant-Whyte, 'Simultaneous Localization, Mapping and Moving Object Tracking,' The International Journal of Robotics Research, vol. 26, pp. 889-916, 2007. [24] A. Pronobis, O. Martinez Mozos, B. Caputo, and P. Jensfelt, 'Multi-modal Semantic Place Classification,' The International Journal of Robotics Research, vol. 29, pp. 298-320, 2010. [25] A. Gupta, S. Satkin, A. A. Efros, and M. Hebert, 'From 3D scene geometry to human workspace,' in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011. [26] G. Debord, Introduction to a critique of urban geography, 1955. [27] C. Nold, 'Emotional Cartography,' Bio Mapping website, 2009. [28] M. Coulson, 'Attributing Emotion to Static Body Postures: Recognition Accuracy, Confusions, and Viewpoint Dependence,' Journal of Nonverbal Behavior, vol. 28, pp. 117-139, 2004. [29] A. Kapur, A. Kapur, N. Virji-Babul, G. Tzanetakis, and P. Driessen, 'Gesture-Based Affective Computing on Motion Capture Data,' in Affective Computing and Intelligent Interaction, 2005. [30] N. Sebe, I. Cohen, and T. S. Huang, 'Multimodal emotion recognition,' Handbook of Pattern Recognition and Computer Vision, vol. 4, pp. 387-419, 2005. [31] T. Baltrusaitis, D. McDuff, N. Banda, M. Mahmoud, R. el Kaliouby, P. Robinson, and R. Picard, 'Real-time inference of mental states from facial expressions and upper body gestures,' in IEEE Int. Conf. on Automatic Face & Gesture Recognition and Workshops, 2011. [32] B. Yao and F.-F. Li, 'Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 99, 2012. [33] T. Lan, Y. Wang, W. Yang, S. Robinovitch, and G. Mori, 'Discriminative Latent Models for Recognizing Contextual Group Activities,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. PP, pp. 1-1, 2011. [34] R. Poppe, 'A survey on vision-based human action recognition,' Image and Vision Computing, vol. 28, pp. 976-990, 2010. [35] The 12 Dramatic Elements. Available: http://www.thedramateacher.com/dramatic-elements/ [36] Elements of Drama - Notes. Available: http://www.slideshare.net/cathtallks/elements-of-drama-notes [37] T. Hofmann, 'Unsupervised Learning by Probabilistic Latent Semantic Analysis,' Machine Learning, vol. 42, pp. 177-196, 2001. [38] S. Bullon, 'Longman dictionary of contemporary English,' ed: Pearson Longman, 2009. [39] D. W. Hosmer and S. Lemeshow, Applied Logistic Regression (2nd ed.): Wiley, 2000. [40] P. H. Wicksteed, The Common Sense of Political Economy, 1910. [41] K. Jarrett, K. Kavukcuoglu, M. A. Ranzato, and Y. LeCun, 'What is the best multi-stage architecture for object recognition?,' in IEEE 12th Int. Conf. on Computer Vision, 2009. [42] Y. Jianchao, Y. Kai, G. Yihong, and T. Huang, 'Linear spatial pyramid matching using sparse coding for image classification,' in IEEE Conf. on Computer Vision and Pattern Recognition, 2009. [43] A. E. Johnson and M. Hebert, 'Using spin images for efficient object recognition in cluttered 3D scenes,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 21, pp. 433-449, 1999. [44] R. B. Rusu, Z. C. Marton, N. Blodow, and M. Beetz, 'Learning informative point classes for the acquisition of object model maps,' in Int. Conf. on Control, Automation, Robotics and Vision., 2008. [45] H. Zou and T. Hastie, 'Regularization and variable selection via the elastic net,' Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 67, pp. 301-320, 2005. [46] D. Bertsekas, Nonlinear Programming: Athena Scientific, 1999. [47] K. Lai, B. Liefeng, R. Xiaofeng, and D. Fox, 'A large-scale hierarchical multi-view RGB-D object dataset,' in IEEE Int. Conf. on Robotics and Automation (ICRA), 2011. [48] Y. L. Boureau, F. Bach, Y. LeCun, and J. Ponce, 'Learning mid-level features for recognition,' in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2010. [49] Y. L. Boureau, J. Ponce, and Y. LeCun, 'A theoretical analysis of feature pooling in visual recognition,' in ICML, 2010. [50] J. Yang and M. H. Yang, 'Learning Hierarchical Image Representation with Sparsity, Saliency and Locality,' BMVC, 2011. [51] L. Itti, C. Koch, and E. Niebur, 'A model of saliency-based visual attention for rapid scene analysis,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 1254-1259, 1998. [52] K.-T. Yu and L.-C. Fu, 'Learning Hierarchical Representation with Sparsity for RGB-D Object Recognition,' in IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2012, to be published. [53] J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake, 'Real-time human pose recognition in parts from single depth images,' in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011. [54] N. Dalal and B. Triggs, 'Histograms of oriented gradients for human detection,' in IEEE Conf. Computer Vision and Pattern Recognition, 2005. [55] C. Thurau and V. Hlavac, 'Pose primitive based human action recognition in videos or still images,' in IEEE Conf. on Computer Vision and Pattern Recognition, 2008. [56] L. Wei-Lwun and J. J. Little, 'Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor,' in The 3rd Canadian Conf. on Computer and Robot Vision, 2006. [57] OpenNI. Available: http://www.openni.org/ [58] Z. Ghahramani, 'Learning dynamic Bayesian networks,' 1998. [59] J. Yang and M. H. Yang, 'Learning Hierarchical Image Representation with Sparsity, Saliency and Locality,' BMVC 2011. [60] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, 'LIBLINEAR: A Library for Large Linear Classification,' J. Mach. Learn. Res., vol. 9, pp. 1871-1874, 2008. [61] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, 'Object Detection with Discriminatively Trained Part-Based Models,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32, pp. 1627-1645, 2010. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65193 | - |
| dc.description.abstract | 服務型機器人在服務使用者時，必須能夠考慮到人的情境狀態，才能自主地提供符合情境的服務。在這篇論文中，我們專注於辦公室的使用者情境，因此定義了六種使用者狀態：專注、疲累、放鬆、休息、社交以及其他。為了要讓機器人從影像觀察中推論使用者的情境，我們提出了一些創新且具鑑別力的特徵。這些特徵包含使用者姿態、人與物體的互動和人與人的互動，而後面兩者為目前較少被用於使用者狀態辨識。
此外，為了更有效地推斷使用者情境，我們提出將使用者情境融入地圖建置。所以使用一個空間-時間的格網地圖來記錄使用者情境與地點和時間的關係。我們使用動態貝氏網路 (Dynamic Bayesian Network) 來作為建置地圖與推論的基礎架構。使用情境地圖架構來推測使用者情境，總共有三項優點 1) 機器人可以動態地根據該區域的使用者情境來調整自己的行為，2) 因為在辦公室環境中，使用者通常有固定的行程，所以使用者行為模式可以從先前的情境觀察中累積，3) 當有多個機器人存在於環境中的不同位置時，可以很容易地分享它們的觀察資訊。 另一方面，機器人的行為決策也整合進同一個架構，形成一個動態決策網路，如此機器人可以隨使用者狀況的變動來規劃要提供適當的服務。實驗部分驗證了使用者情境辨識、地圖建置以及整個系統的有效性。 | zh_TW |
| dc.description.abstract | Robot that services humans must consider human context in order to behave and service properly. Here, the context categories we tailor for office environment include concentrating, tired, relaxed, napping, social, and neutral. We design several novel features for inferring human context from visual observation. The features incorporate human pose, human-object interaction, and human-human interaction, of which the last two have rarely been explored in human status estimation.
Moreover, to infer human context more efficiently, we propose a novel semantic mapping framework that embeds human context into a map representation. This produces a spatial-temporal grid map that represents the relationship of human context with location and time. We construct the framework using Dynamic Bayesian Network. There are three exclusive advantages for building such map: 1) a service robot can dynamically adjust its behavior and plan services based on the estimated context in an area; 2) because in office environment people tend to have fixed schedules, people’s living patterns can be extracted in the map; and 3) multiple robots can easily share their observations on human context at different places. On the other hand, robot behavior decisions are integrated into the framework, which leads to a unified dynamic decision network. Thus, the robot can plan proper services according to the human-context map. The effectiveness of our proposed context recognition, mapping and decision framework is verified with simulation and real testing scenario. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T23:29:18Z (GMT). No. of bitstreams: 1 ntu-101-R99922070-1.pdf: 1999926 bytes, checksum: b116b661d063db9a086782fb54282a5d (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii Abstract iii Table of Contents iv List of Figures vi List of Tables ix Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Related Work 3 1.2.1 Semantic Mapping 3 1.2.2 Human context estimation 8 1.3 Thesis Organization 10 Chapter 2 Human Context from Observation 11 2.1 Human Context in Office Environment 11 2.2 Preliminary tools 14 2.2.1 Nearby region (NR) 15 2.2.2 Proximity index (PI) 15 2.2.3 Logistic model 17 2.3 Human-Object interaction 20 2.3.1 RGB-D Object Recognition using Hierarchical Sparse Descriptor 20 2.3.2 Object detection 26 2.3.3 From Objects to Human Context 27 2.4 Human Pose and Motion 29 2.4.1 From human pose to human context 29 2.4.2 Estimating Human Pose and Motion from Depth Image 31 2.5 Human-Human Interaction 32 Chapter 3 Human-Context Mapping Framework and Behavior decision 33 3.1 Inference from the past 33 3.2 Historical grid mapping 35 3.3 Information Fusion 37 3.4 Behavior decision in Office Environment 38 3.4.1 Service Task 39 3.4.2 Motion Planning 40 3.4.3 Associating Behaviors with human context 41 Chapter 4 Evaluation 43 4.1 Human-context inference from observation 43 4.1.1 Human-object interaction 43 4.1.2 Human-Pose 48 4.1.3 Human-Human Interaction 58 4.2 Human-Context Mapping 61 4.2.1 Simulation 61 4.3 Context Aware Servicing 65 Chapter 5 Conclusion 69 REFERENCE 71 | |
| dc.language.iso | en | |
| dc.subject | 辦公室機器人 | zh_TW |
| dc.subject | 情境感知 | zh_TW |
| dc.subject | 情境地圖建置 | zh_TW |
| dc.subject | human context mapping | en |
| dc.subject | context-aware | en |
| dc.subject | office robot | en |
| dc.title | 以情境地圖達成在辦公室環境中以人為中心的服務 | zh_TW |
| dc.title | Human-Context Mapping for Human-Centric Robot Service in Office Environment | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李蔡彥(Tsai-Yen Li),簡忠漢(Jong-Hann Jean),黃正民(Huang Cheng-Ming),王傑智(Chieh-Chih Wang) | |
| dc.subject.keyword | 情境地圖建置,情境感知,辦公室機器人, | zh_TW |
| dc.subject.keyword | human context mapping,context-aware,office robot, | en |
| dc.relation.page | 74 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-07-31 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-101-1.pdf 未授權公開取用 | 1.95 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。