Abstract
In today's world, we have increasingly sophisticated means to record the movement of humans and other moving objects in the form of trajectory data. These data are being accumulated at an extremely fast rate. As a result, knowledge discovery from these data for recognizing activities has become an important problem. The discovered activity patterns can help us understand people's lives, analyze traffic in a large city and study social networks among people. Trajectory-based activity recognition builds upon some fundamental functions of location estimation and machine learning, and can provide new insights on how to infer high-level goals and objectives from low-level sensor readings. In this chapter, we survey the area of trajectory-based activity recognition. We start from research in location estimation from sensors for obtaining the trajectories. We then review trajectory-based activity recognition research. We classify the research work on trajectory-based activity recognition into several broad categories, and systematically summarize existing work as well as future works in light of the categorization.
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References
Bahl, P., Padmanabhan, V.N.: Radar: An in-building rf-based user location and tracking system. In: Proc. The Annual IEEE International Conference on Computer Communications (INFOCOM), pp. 775–784 (2000)
Barnard, K., Duygulu, P., Forsyth, D.A., de Freitas, N., Blei, D.M., Jordan, M.I.: Matching words and pictures. Journal of Machine Learning Research 3 (2003)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Boyd, S., Vandenberghe, L.: Convex optimization. Cambridge Univ Pr (2004)
Brand, M., Oliver, N., Pentland, A.: Coupled hidden markov models for complex action recognition. In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR 97), CVPR 97, p. 994. IEEE Computer Society, Washington, DC, USA (1997)
Breiman, L.: Classification and regression trees. Chapman & Hall/CRC (1984)
Cao, H., Mamoulis, N., Cheung, D.: Mining frequent spatio-temporal sequential patterns. In: Proc. of IEEE International Conference on Data Mining (ICDM), pp. 8–pp. IEEE (2005)
Doucet, A., De Freitas, N., Gordon, N.: Sequential Monte Carlo methods in practice. Springer Verlag (2001)
Doucet, A., Godsill, S., Andrieu, C.: On sequential monte carlo sampling methods for bayesian filtering. Statistics and computing 10(3), 197–208 (2000)
Eagle, N., Pentland, A.: Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology 63(7), 1057–1066 (2009)
Eagle, N., (Sandy) Pentland, A.: Reality mining: sensing complex social systems. Personal Ubiquitous Comput. 10, 255–268 (2006)
Farrahi, K., Gatica-Perez, D.: Discovering routines from large-scale human locations using probabilistic topic models. ACM Transactions on Intelligent Systems and Technology (TIST) 2(1), 3 (2011)
Hofmann, T., Puzicha, J.: Latent class models for collaborative filtering. In: Proc. of the 16th International Joint Conference on Artificial Intelligence (IJCAI 99), pp. 688–693 (1999)
Hofmann-Wellenhof, B., Lichtenegger, H., Collins, J.: Global positioning System. Theory and Practice. (1993)
Huynh, T., Fritz, M., Schiele, B.: Discovery of activity patterns using topic models. In: Proc. of International Conference on Ubiquitous Computing (UbiComp), pp. 10–19 (2008)
Huynh, T., Schiele, B.: Analyzing features for activity recognition. p. 159C163. Smart objects and ambient in- telligence: innovative context-aware services (2005)
van Kasteren, T., Englebienne, G., Kr¨ose, B.J.A.: Transferring knowledge of activity recognition across sensor networks. In: Proc. of the International Conference of Pervasive Computing, pp. 283–300 (2010)
Krumm, J. (ed.): Ubiquitous Computing Fundamentals. Chapman and Hall/CRC, Boca Raton, FL (2010)
Ladd, A.M., Bekris, K.E., Rudys, A., Kavraki, L.E., Wallach, D.S., Marceau, G.: Roboticsbased location sensing using wireless ethernet. In: Proc. of The Annual International Conference on Mobile Computing and Networking (MobiCom), pp. 227–238 (2002)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. The International Conference on Machine Learning (ICML), pp. 282–289 (2001)
Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: Proc. of the ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 1099–1108 (2010)
Li, Z., Han, J., Ji, M., Tang, L.A., Yu, Y., Ding, B., Lee, J.G., Kays, R.: Movemine: Mining moving object data for discovery of animal movement patterns. ACM Transactions on Intelligent Systems and Technology (ACM TIST) (Special Issue on Computational Sustainability) (Aug. 2010)
Lian, C.C., Hsu, J.Y.j.: Probabilistic models for concurrent chatting activity recognition. In: Proc. of the 21st International Jont Conference on Artifical Jntelligence (IJCAI 09) (2009)
Liao, L., Fox, D., Kautz, H.A.: Extracting places and activities from gps traces using hierarchical conditional random fields. I. J. Robotic Res. 26(1), 119–134 (2007)
Liao, L., Patterson, D.J., Fox, D., Kautz, H.A.: Learning and inferring transportation routines. Artif. Intell. 171(5-6), 311–331 (2007)
Liu, S., Liu, Y., Ni, L.M., 0002, J.F., Li, M.: Towards mobility-based clustering. In: Proc. of the ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 919–928 (2010)
Miluzzo, E., Cornelius, C., Ramaswamy, A., Choudhury, T., Liu, Z., Campbell, A.T.: Darwin phones: the evolution of sensing and inference on mobile phones. In: Proc. The Annual International Conference on Mobile Systems (MobiSys), pp. 5–20 (2010)
Murphy, K.: Dynamic bayesian networks: representation, inference and learning. Ph.D. thesis, UC Berkeley, Computer Science Division (2002)
Nazerfard, E., Rashidi, P., Cook, D.J.: Using association rule mining to discover temporal relations of daily activities
Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: Landmarc: Indoor location sensing using active rfid. Wireless Networks 10(6), 701–710 (2004)
Pan, J.J., Yang, Q., Pan, S.J.: Online co-localization in indoor wireless networks by dimension reduction. In: Proc. of National Conference on Artificial Intelligence (AAAI), pp. 1102–1107 (2007)
Patterson, D.J., Liao, L., Fox, D., Kautz, H.A.: Inferring high-level behavior from low-level sensors. In: Proc. of International Conference on Ubiquitous Computing (UbiComp), pp. 73–89 (2003)
Quinlan, J.: C4. 5: programs for machine learning. Morgan Kaufmann (1993)
Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M.H., Srivastava, M.B.: Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks (TOSN) 6(2) (2010)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proc. of InternationalWorldWideWeb Conference, pp. 851–860 (2010)
Schulz, D., Fox, D., Hightower, J.: People tracking with anonymous and id-sensors using rao-blackwellised particle filters. In: Proc. of International Joint Conferences on Artificial Intelligence (IJCAI), pp. 921–928 (2003)
Sutton, C.A., Rohanimanesh, K., McCallum, A.: Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. In: Proc. The International Conference on Machine Learning (ICML) (2004)
Thiagarajan, A., Ravindranath, L., LaCurts, K., Madden, S., Balakrishnan, H., Toledo, S., Eriksson, J.: Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of cognitive neuroscience 3(1), 71–86 (1991)
Vail, D.L., Veloso, M.M., Lafferty, J.D.: Conditional random fields for activity recognition. In: Proc. the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), p. 235 (2007)
Wang, L., Gu, T., Tao, X., Lu, J.: Sensor-based human activity recognition in a multi-user scenario. In: Proc. of the International Joint Conference on Ambient Intelligence (AmI), pp. 78–87 (2009)
Yang, Q., Pan, S.J., Zheng, V.W.: Estimating location using wi-fi. IEEE Intelligent Systems 23(1), 8–13 (2008)
Yin, J., Chai, X., Yang, Q.: High-level goal recognition in a wireless lan. In: Proc. of National Conference on Artificial Intelligence (AAAI), pp. 578–584 (2004)
Yin, J., Shen, D., Yang, Q., Li, Z.N.: Activity recognition through goal-based segmentation. In: Proc. of National Conference on Artificial Intelligence (AAAI), pp. 28–34 (2005)
Zheng, V.W., Yang, Q.: User-dependent aspect model for collaborative activity recognition. In: In Proc. of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11) (2011)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.Y.: Understanding mobility based on gps data. In: Proc. of International Conference on Ubiquitous Computing (UbiComp), pp. 312–321 (2008)
Zheng, Y., Xie, X.: Learning travel recommendations from user-generated gps traces. ACM Transactions on Intelligent Systems and Technology (TIST) 2(1), 2 (2011)
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Zhu, Y., Zheng, V.W., Yang, Q. (2011). Activity Recognition from Trajectory Data. In: Zheng, Y., Zhou, X. (eds) Computing with Spatial Trajectories. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1629-6_6
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DOI: https://doi.org/10.1007/978-1-4614-1629-6_6
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