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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

1. Introduction

verfasst von : Zhiwen Yu, Zhu Wang

Erschienen in: Human Behavior Analysis: Sensing and Understanding

Verlag: Springer Singapore

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Abstract

Human behavior sensing and understanding has been a popular research area during the past decades, which plays an important role in human-computer interaction and public security. It aims to understand what people are doing by sensing and recognizing their activities and their environments. However, accurate detection and recognition of human behavior is still a big challenge that attracts a lot of research efforts. In this chapter, we aim to present an overview of human behavior sensing and understanding techniques, including from vision-based to sensor-based and device-free behavior sensing, from individual to group and community behavior understanding, and from pattern-based to model-based behavior understanding.

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Nächstes Kapitel Main Steps of Human Behavior Sensing and Understanding
Literatur
1.
M. Weiser, “The computer for the twenty-first century,” Sci Am., vol. 265, no. 3, pp. 94–104, 1991.CrossRef
2.
T. Choudhury, S. Consolvo, and B. Harrison, “The mobile sensing platform: An embedded activity recognition system,” IEEE Pervasive Comput., vol. 7, no. 2, pp. 32–41, Apr./Jun. 2008.CrossRef
3.
J.K. Aggarwal and Q. Cai, “Human motion analysis: A review,” Comput. Vis. Image Underst., vol. 73, no. 3, pp. 428–440, 1999.CrossRef
4.
C. Cedras and M. Shah, “Motion-based recognition: A survey,” Image Vis. Comput., vol. 13, no. 2, pp. 129–155, 1995.CrossRef
5.
D.M. Gavrila, “The visual analysis of human movement: A survey,” Comput. Vis. Image Underst., vol. 73, no. 1, pp. 82–98, 1999.MATHCrossRef
6.
R. Poppe, “A survey on vision-based human action recognition,” Image Vis. Comput., vol. 28, no. 6, pp. 976–990, 2010.CrossRef
7.
T.B. Moeslund, A. Hilton, and V. Kruger, “A survey of advances in vision-based human motion capture and analysis,” Comput. Vis. Image Underst., vol. 104, no. 2, pp. 90–126, 2006.CrossRef
8.
A. Yilmaz, O. Javed, and M. Shah, “Object tracking: A survey,” ACM Comput. Surv., vol. 38, no. 4, pp. 1–45, 2006.CrossRef
9.
P. Turaga, R. Chellappa, and O. Udrea, “Machine recognition of human activities: A survey,” IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 11, pp. 1473–1488, Nov. 2008.CrossRef
10.
D. Weinland, R. Ronfard, and E. Boyer, “A survey of vision-based methods for action representation, segmentation and recognition,” Comput. Vis. Image Underst., vol. 115, no. 2, pp. 224–241, 2011.CrossRef
11.
K. Laerhoven and K. A. Aidoo, “Teaching context to applications,” J. Pers. Ubiquitous Comput., vol. 5, no. 1, pp. 46–49, 2001.CrossRef
12.
C. R. Wren and E. M. Tapia, “Toward scalable activity recognition for sensor networks,” in Proc. 2nd Int. Workshop Location Context Awareness, 2006, pp. 168–185.
13.
M. Stikicand and B. Schiele, “Activity recognition from sparsely labeled data using multi-instance learning,” in Proc. Location Context Awareness, 2009, vol. 5561, pp. 156–173.
14.
M. Philipose, K.P. Fishkin, M. Perkowitz, D.J. Patterson, D. Fox, H. Kautz, and D. Hahnel, “Inferring activities from interactions with objects,” IEEE Pervasive Comput., vol. 3, no. 4, pp. 50–57, Oct./Dec. 2004.CrossRef
15.
D. Cook and M. Schmitter-Edgecombe, “Assessing the quality of activities in a smart environment,” Methods Inf. Med., vol. 48, no. 5, pp. 480–485, 2009.CrossRef
16.
T. Kasterenand and B. Krose, “Bayesian activity recognition in residence for elders,” in Proc. Int. Conf. Intell. Environ., Feb. 2008, pp. 209–212.
17.
L. Chen and C. D. Nugent, “Ontology-based activity recognition in intelligent pervasive environments,” Int. J. Web Inf. Syst., vol. 5, no. 4, pp. 410–430, 2009.CrossRef
18.
G. Singla, D. Cook, and M. Schmitter-Edgecombe, “Recognizing independent and joint activities among multiple residents in smart environments,” J. Ambient Intell. Humaniz Comput., vol. 1, no. 1, pp. 57–63, 2010.CrossRef
19.
J. Sung, C. Ponce, B. Selman, and A. Saxena. 2011. Human activity detection from RGBD images. In Proceedings of the AAAI Workshop on Plan, Activity, and Intent Recognition.
20.
Jonathan Lester, Tanzeem Choudhury, and Gaetano Borriello. 2006. A practical approach to recognizing physical activities. In Proceedings of the International Conference on Pervasive Computing. 1–16.
21.
Oliver Amft, Martin Kusserow, and Gerhard Troster. 2007. Probabilistic parsing of dietary activity events. In Proceedings of BSN. Springer, 242–247.
22.
G. Pirkl, K. Stockinger, K. Kunze, and P. Lukowicz. 2008. Adapting magnetic resonant coupling based relative positioning technology for wearable activity recognition. In Proceedings of ISWC. 47–54.
23.
D. Wan. 1999. Magic medicine cabinet: A situated portal for consumer healthcare. In Handheld and Ubiquitous Computing. Springer, 352–355.
24.
R. de Oliveira, M. Cherubini, and N. Oliver. 2010. MoviPill: Improving medication compliance for elders using a mobile persuasive social game. In Proceedings of UbiComp, Vol. 1001. 36.
25.
J. Krumm and E. Horvitz. 2006. Predestination: Inferring destinations from partial trajectories. In Proceedings of UbiComp. 243–260.
26.
M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,” in Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp. 1395–1402, Beijing, China, 2005.
27.
I. Laptev and T. Lindeberg, “Space-time interest points,” in Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 432–439, Nice, France, 2003.
28.
A. Jalal, Y. Kim, Y. Kim, et al. Robust human activity recognition from depth video using spatiotemporal multi-fused features. Pattern recognition, 2017, 61: 295–308.CrossRef
29.
X. Yang and Y. Tian. Super normal vector for human activity recognition with depth cameras. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(5): 1028–1039.CrossRef
30.
A. Jordan, “On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes,” Advances in neural information processing systems, vol. 14, p. 841, 2002.
31.
S. Min, B. Lee, and S. Yoon, “Deep learning in bioinformatics,” Briefings in Bioinformatics, vol. 17, 2016.
32.
G. Luo, S. Dong, K. Wang, and H. Zhang, “Cardiac left ventricular volumes prediction method based on atlas location and deep learning,” in 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1604–1610, Shenzhen, China, 2016
33.
A. Pantelopoulos and N. G. Bourbakis, “A survey on wearable sensor-based systems for health monitoring and prognosis,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 40, no. 1, pp. 1–12, Jan. 2010.
34.
H. Alemdar and C. Ersoy, “Wireless sensor networks for healthcare: A survey,” Comput. Netw., vol. 54, no. 15, pp. 2688–2710, 2010.CrossRef
35.
D. Ding, R. A. Cooper, P. F. Pasquina, and L. Fici-Pasquina, “Sensor technology for smart homes,” Maturitas, vol. 69, no. 2, pp. 131–136, 2011.CrossRef
36.
Y. Lu, Y. Wei, L. Liu, et al. Towards unsupervised physical activity recognition using smartphone accelerometers. Multimedia Tools and Applications, 2017, 76(8): 10701–10719.CrossRef
37.
J. Krumm, S. Harris, B. Meyers, B. Brumitt, M. Hale, and S. Shafer. Multi-camera multi-person tracking for easy living. In Proceedings of Third IEEE International Workshop on Visual Surveillance, IWVS’ 00, pages 3–10, 2000.
38.
M. Valtonen, J. Maentausta, and J. Vanhala. Tiletrack: Capacitive human tracking using floor tiles. In IEEE International Conference on Pervasive Computing and Communications, PerCom’09, pages 1–10, 2009.
39.
R. J. Orr and G. D. Abowd. The smart floor: a mechanism for natural user identification and tracking. In CHI ‘00 extended abstracts on Human factors in computing systems, CHI EA ‘00, pages 275–276, 2000.
40.
D. De, W. Song, M. Xu, C. Wang, D. Cook, and X. Huo. Findinghumo: Real-time tracking of motion trajectories from anonymous binary sensing in smart environments. In Proceedings of the 32nd IEEE International Conference on Distributed Computing Systems, ICDCS’12, pages 163–172, 2012.
41.
T.W. Hnat, E. Griffiths, R. Dawson, and K. Whitehouse. Doorjamb: unobtrusive room-level tracking of people in homes using doorway sensors. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, SenSys’12, pages 309–322, 2012.
42.
Wang W, Liu A X, Shahzad M, et al. Device-free human activity recognition using commercial WiFi devices[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(5): 1118–1131.CrossRef
43.
Li T, Chang H, Wang M, et al. Crowded scene analysis: A survey IEEE transactions on circuits and systems for video technology, 2015, 25(3): 367–386.CrossRef
44.
Zhang, D., Guo, B., & Yu, Z. (2011). The emergence of social and community intelligence. IEEE Computer, 44(7), 21–28.CrossRef
45.
Su H, Dong Y, Zhu J, et al. Crowd Scene Understanding with Coherent Recurrent Neural Networks[C]. IJCAI. 2016, 1: 2.
46.
Zhuang N, Yusufu T, Ye J, et al. Group activity recognition with differential recurrent convolutional neural networks[C]. Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on. IEEE, 2017: 526–531.
47.
Freeman, L. C. (2004). The development of social network analysis: A study in the sociology of science. Empirical Press.
48.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge, UK: Cambridge University Press.MATHCrossRef
49.
McCallum, A., Wang, X., & Corrada-Emmanuel, A. (2007). Topic and role discovery in social networks with experiments on Enron and academic email. Journal of Artificial Intelligence Research. 30(1), 249–272.CrossRef
50.
Barabasi, A. L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Statistical Mechanics and its Applications, 311 (3–4), 590–614.MathSciNetMATHCrossRef
51.
Tang, J., Jin, R. M., & Zhang, J. (2008). A topic modeling approach and its integration into the random walk framework for academic search. Paper presented at the Meeting of 2008 IEEE International Conference on the Meeting of 2008 IEEE International Conference on Data Mining. Pisa, Italy.
52.
Sheth, A. (2010). Computing for human experience – Semantics-empowered sensors, services, and social computing on the ubiquitous web. IEEE Internet Computing, 14 (1), 88–97.CrossRef
53.
Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes Twitter users: Real-time event detection by social sensors. Paper presented at the Meeting of WWW 2010 Conference. Raleigh, NC.
54.
Bollen, J., Pepe, A., & Mao, H. (2009). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. Paper presented at the meeting of WWW 2009 Conference. Madrid, Spain.
55.
Calabrese, F., & Ratti, C. (2006). Real time Rome. Networks and Communications Studies, 20(3–4), 247–258.
56.
Nicholson, J., & Noble, B. D. (2008). Bread crumbs: Forecasting mobile connectivity. Paper presented at the Meeting of Mobile Computing and Networking. San Francisco, CA.
57.
Zhang C, Zhang K, Yuan Q, et al. Regions, periods, activities: Uncovering urban dynamics via cross-modal representation learning[C]//Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017: 361–370.
58.
Wang Z, Guo B, Yu Z, et al. Wi-Fi CSI-Based Behavior Recognition: From Signals and Actions to Activities. IEEE Communications Magazine, 2018, 56(5): 109–115.CrossRef
59.
Wu D, Zhang D, Xu C, Wang H, Li X (2017) Device-free Wi-Fi human sensing: from pattern-based to model-based approaches. IEEE Communications Magazine, 55(10): 91–97.CrossRef
60.
Chen L, Hoey J, Nugent C D, et al. Sensor-Based Activity Recognition, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(6):790–808.CrossRef
Metadaten
Titel
Introduction
verfasst von
Zhiwen Yu
Zhu Wang
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Human Behavior Analysis: Sensing and Understanding

Print ISBN: 978-981-15-2108-9

Electronic ISBN: 978-981-15-2109-6

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-2109-6_1