Learning People Movement Model from Multiple Cameras for Behaviour Recognition

  • Nam T. Nguyen
  • Svetha Venkatesh
  • Geoff A. W. West
  • Hung H. Bui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)


In surveillance systems for monitoring people behaviours, it is important to build systems that can adapt to the signatures of people’s tasks and movements in the environment. At the same time, it is important to cope with noisy observations produced by a set of cameras with possibly different characteristics. In previous work, we have implemented a distributed surveillance system designed for complex indoor environments [1]. The system uses the Abstract Hidden Markov mEmory Model (AHMEM) for modelling and specifying complex human behaviours that can take place in the environment. Given a sequence of observations from a set of cameras, the system employs approximate probabilistic inference to compute the likelihood of different possible behaviours in real-time. This paper describes the techniques that can be used to learn the different camera noise models and the human movement models to be used in this system. The system is able to monitor and classify people behaviours as data is being gathered, and we provide classification results showing the system is able to identify behaviours of people from their movement signatures.


  1. 1.
    Nguyen, N.T., Bui, H.H., Venkatesh, S., West, G.: Recognising and monitoring high-level behaviours in complex spatial environments. In: IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, pp. 620–625 (2003)Google Scholar
  2. 2.
    Oliver, N., Horvitz, E., Garg, A.: Layered representations for human activity recognition. In: Proceedings of the Fourth IEEE International Conference on Multimodal Interfaces, pp. 3–8 (2002)Google Scholar
  3. 3.
    Ivanov, Y., Bobick, A.: Recognition of visual activities and interactions by stochastic parsing. IEEE Transactions on Pattern Recognition and Machine Intelligence 22, 852–872 (2000)CrossRefGoogle Scholar
  4. 4.
    Galata, A., Johnson, N., Hogg, D.: Learning variable length Markov models of behaviour. Intenational Journal of Computer Vision and Image Understanding 81, 398–413 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Hoey, J.: Hierarchical unsupervised learning of event categories. In: IEEE Workshop on Detection and Recognition of Events in Video, Vancouver, Canada, pp. 99–106 (2001)Google Scholar
  6. 6.
    Bui, H.H.: A general model for online probabilistic plan recognition. In: The 18th International Joint Conference on Artificial Intelligence (IJCAI 2003), Acapulco, Mexico (2003)Google Scholar
  7. 7.
    Pynadath, D.V., Wellman, M.P.: Generalized queries on probabilistic context-free grammars. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 65–77 (1998)CrossRefGoogle Scholar
  8. 8.
    Bui, H.H., Venkatesh, S., West, G.: Policy recognition in the Abstract HiddenMarkovModel. Journal of Artificial Intelligence Research 17, 451–499 (2002)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Doucet, A., de Freitas, N., Murphy, K., Russell, S.: Rao-Blackwellised particle filtering for dynamic Bayesian networks. In: Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence, Stanford, California, pp. 176–183. AAAI Press, Menlo Park (2000)Google Scholar
  10. 10.
    Bui, H.H.: Efficient approximate inference for online probabilistic plan recognition. In: AAAI Fall Symposium on Intent Inference for Users, Teams and Adversaries, Falmouth, Massachusetts (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Nam T. Nguyen
    • 1
  • Svetha Venkatesh
    • 1
  • Geoff A. W. West
    • 1
  • Hung H. Bui
    • 2
  1. 1.Department of ComputingCurtin University of TechnologyPerth
  2. 2.Artificial Intelligence CenterSRI InternationalMenlo ParkUSA

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