Behaviour Recognition using the Event Calculus

  • Alexander Artikis
  • George Paliouras
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)

Abstract

We present a system for recognising human behaviour given a symbolic representation of surveillance videos. The input of our system is a set of timestamped short-term behaviours, that is, behaviours taking place in a short period of time — walking, running, standing still, etc — detected on video frames. The output of our system is a set of recognised long-term behaviours — fighting, meeting, leaving an object, collapsing, walking, etc — which are pre-defined temporal combinations of short-term behaviours. The definition of a long-term behaviour, including the temporal constraints on the short-term behaviours that, if satisfied, lead to the recognition of the long-term behaviour, is expressed in the Event Calculus. We present experimental results concerning videos with several humans and objects, temporally overlapping and repetitive behaviours.

Keywords

Inductive Logic Programming Behaviour Recognition Temporal Combination Walk Away Complex Event Processing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    D. Alrajeh, O. Ray, A. Russo, and S. Uchitel. Extracting requirements from scenarios with ILP. In Inductive Logic Programming, volume LNAI 4455. Springer, 2007.Google Scholar
  2. 2.
    A. Artikis, M. Sergot, and J. Pitt. Specifying norm-governed computational societies. ACM Transactions on Computational Logic, 10(1), 2009.Google Scholar
  3. 3.
    K. Clark. Negation as failure. In H. Gallaire and J. Minker, editors, Logic and Databases, pages 293–322. Plenum Press, 1978.Google Scholar
  4. 4.
    C. Dousson and P. Le Maigat. Chronicle recognition improvement using temporal focusing and hierarchisation. In Proceedings International Joint Conference on Artificial Intelligence (IJCAI), pages 324–329, 2007.Google Scholar
  5. 5.
    M. Ghallab. On chronicles: Representation, on-line recognition and learning. In Proceedings Conference on Principles of Knowledge Representation and Reasoning, pages 597–606, 1996.Google Scholar
  6. 6.
    R. Kowalski and M. Sergot. A logic-based calculus of events. New Generation Computing, 4(1):67–96, 1986.CrossRefGoogle Scholar
  7. 7.
    D. Luckham. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley, 2002.Google Scholar
  8. 8.
    R. Miller and M. Shanahan. The event calculus in a classical logic — alternative axiomatizations. Journal of Electronic Transactions on Artificial Intelligence, 4(16), 2000.Google Scholar
  9. 9.
    M. Shanahan. The event calculus explained. In M. Wooldridge and M. Veloso, editors, Artificial Intelligence Today, LNAI 1600, pages 409–430. Springer, 1999.Google Scholar
  10. 10.
    V.-T. Vu. Temporal Scenarios for Automatic Video Interpretation. PhD thesis, Université de Nice — Sophia Antipolis, 2004.Google Scholar
  11. 11.
    V.-T. Vu, F. Brémond, and M. Thonnat. Automatic video interpretation: A novel algorithm for temporal scenario recognition. In Proceedings International Joint Conference on Artificial Intelligence, pages 1295–1302, 2003.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Alexander Artikis
    • 1
  • George Paliouras
    • 1
  1. 1.Institute of Informatics and TelecommunicationsNational Centre for Scientific Research “Demokritos”AthensGreece

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