Open Access
Issue
ITM Web of Conferences
Volume 1, 2013
ACTIMS 2012 – Activity-Based Modeling & Simulation 2012
Article Number 02002
Number of page(s) 8
Section Innovative Concepts
DOI https://doi.org/10.1051/itmconf/20130102002
Published online 29 November 2013
  1. X. Hu, Dynamic Data Driven Simulation, SCS M&S Magazine, (2011) [Google Scholar]
  2. H. Xue, F. Gu, X. Hu, Data Assimilation Using Sequential Monte Carlo Methods in Wildfire Spread Simulation, TOMACS, (2012). [Google Scholar]
  3. A. Doucet,, N. D. Freitas, N. Gordon (eds.), Sequential Monte Carlo methods in practice, Springer, (2001) [Google Scholar]
  4. L. Rabiner, B. Juang, An introduction to hidden Markov models, ASSP Magazine, IEEE, vol.3, no.1, pp.4–16, (1986) [Google Scholar]
  5. W. Hoff, J. Howard, Recognition in a dense sensor network, SNA, (2009) [Google Scholar]
  6. L. Kratz, K. Nishino. Anomaly Detection in Extremely Crowded Scenes Using Spatio-Temporal Motion Pattern Models, CVPR, pages 1446–1453, (2009) [Google Scholar]
  7. L. Wang, T. Gu, X. Tao, J. Lu, Sensor-based human activity recognition in a multi-user scenario, in Ambient Intelligence, volume 5859, chapter 10, pages 78±87. Springer Berlin Heidelberg, Berlin, Heidelberg, (2009) [Google Scholar]
  8. D. De, W. Z. Song, M. Xu, D. Cook, X. Huo, FindingHuMo: Real-Time Tracking of Motion Trajectories from Anonymous Binary Sensing in Smart Environments, ICDCS, (2012). [Google Scholar]
  9. M. Brand, Coupled Hidden Markov Models for Modeling Interacting Processes, Technical report, MIT, (1996) [Google Scholar]
  10. A. Nefian, L. Liang, X. Pi, X. Liu, C. Mao, K. Murphy, A coupled HMM for audio-visual speech recognition. In Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, pp. 2013–2016, Orlando, Fla, USA, (2002) [Google Scholar]
  11. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, John Wiley & Sons, New York, NY, USA, 2nd edition, (2000) [Google Scholar]