Abstract
Mobile devices have become pervasive among users in both work environments as well as everyday life, and they sense a wealth of information that can be exploited for a variety of tasks, such as activity recognition, security or health monitoring. In this paper, we explore the feasibility of trajectory clustering, i.e., detecting similarities between moving objects, for an application related to workplace productivity improvement. We use Hierarchical Dirichlet Processes due to their ability to automatically extract appropriate trajectory segments. The application domain is the analysis of RSSI data, where this machine learning method proves successfully.
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References
Nascimento, J.C., Figueiredo, M., Marques, J.S.: Trajectory classification using switched dynamical hidden markov models. IEEE Transactions on Image Processing 19(5), 1338–1348 (2010)
Bennewitz, M., Burgard, W., Thrun, S.: Using em to learn motion behaviors of persons with mobile robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 502–507. IEEE (2002)
Atev, S., Miller, G., Papanikolopoulos, N.P.: Clustering of vehicle trajectories. IEEE Transactions on Intelligent Transportation Systems 11(3), 647–657 (2010)
Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 1135–1138. IEEE (2006)
Dong, W., Olguin-Olguin, D., Waber, B., Kim, T., Pentland, P.: Mapping organizational dynamics with body sensor networks. In: 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 130–135. IEEE (2012)
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical dirichlet processes. Journal of the American Statistical Association 101(476) (2006)
Olguin, D.O., Waber, B.N., Kim, T., Mohan, A., Ara, K., Pentland, A.: Sensible organizations: Technology and methodology for automatically measuring organizational behavior. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(1), 43–55 (2009)
Lee, J.-G., Han, J., Li, X., Gonzalez, H.: Traclass: Trajectory classification using hierarchical region-based and trajectory-based clustering. Proceedings of the VLDB Endowment 1(1), 1081–1094 (2008)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Teh, Y.W., Jordan, M.I.: Hierarchical Bayesian nonparametric models with applications. In: Hjort, N., Holmes, C., Muller, P., Walker, S. (eds.) Bayesian Nonparametrics: Principles and Practice. Cambridge University Press (2010)
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Ghourchian, N., Precup, D. (2014). Analyzing User Trajectories from Mobile Device Data with Hierarchical Dirichlet Processes. In: Sokolova, M., van Beek, P. (eds) Advances in Artificial Intelligence. Canadian AI 2014. Lecture Notes in Computer Science(), vol 8436. Springer, Cham. https://doi.org/10.1007/978-3-319-06483-3_10
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DOI: https://doi.org/10.1007/978-3-319-06483-3_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06482-6
Online ISBN: 978-3-319-06483-3
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