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Propagating Uncertainty in Petri Nets for Activity Recognition

  • Gal Lavee
  • Michael Rudzsky
  • Ehud Rivlin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6454)

Abstract

Petri Nets is a formalism that has recently been proposed for the specification of models for use in activity recognition . This formalism is attractive because of its inherent ability to model partial ordering, concurrency, logical and temporal relations between the events that compose activities. The main novelty of this work is a probabilistic mechanism (based on the particle filter) for recognizing activities modeled as Petri Nets in video. This mechanism takes into account the observation and semantic uncertainty inherent in low-level events and propagates it into a probabilistic activity recognition.

Keywords

Particle Filter Activity Recognition Proposal Distribution Reachable State Place Node 
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.
    Tessier, C.: Towards a commonsense estimator for activity tracking. Technical Report SS-03-05, AAAI (2003)Google Scholar
  2. 2.
    Lavee, G., Rudzsky, M., Rivlin, E., Borzin, A.: Video event modeling and recognition in generalized stochastic petri nets. Circuits and Sytems for Video Technology 20, 102–118 (2010)CrossRefGoogle Scholar
  3. 3.
    Albanese, M., Chellappa, R., Moscato, V., Picariello, A., Subrahmanian, V.S., Turaga, P., Udrea, O.: A constrained probabilistic petri net framework for human activity detection in video. IEEE Transactions on Multimedia 10, 982–996 (2008)CrossRefGoogle Scholar
  4. 4.
    Perše, M., Kristan, M., Perš, J., Mušič, G., Vučkovič, G., Kovačič, S.: Analysis of multi-agent activity using petri nets. Pattern Recogn. 43, 1491–1501 (2010)CrossRefzbMATHGoogle Scholar
  5. 5.
    Vu, V.T., Bremond, F., Thonnat, M.: Automatic video interpretation: a novel algorithm for temporal scenario recognition. In: IJCAI 2003, pp. 1295–1300. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
  6. 6.
    Ivanov, Y., Bobick, A.: Recognition of visual activities and interactions by stochastic parsing. In: CVPR 1998, vol. 22, p. 852 (1998)Google Scholar
  7. 7.
    Gerber, R., Nagel, H.H.: Representation of occurrences for road vehicle traffic. Artif. Intell. 172, 351–391 (2008)CrossRefGoogle Scholar
  8. 8.
    Fernández, C., Baiget, P., Roca, X., González, J.: Interpretation of complex situations in a semantic-based surveillance framework. Image Commun. 23 (2008)Google Scholar
  9. 9.
    Shi, Y., Huang, Y., Minnen, D., Bobick, A., Essa, I.: Propagation networks for recognition of partially ordered sequential action. In: CVPR, vol. 02, pp. 862–869 (2004)Google Scholar
  10. 10.
    Lavee, G., Borzin, A., Rudzsky, M., Rivlin, E.: Building Petri Nets from video event ontologies. In: International Symposium on Visual Computing (2007)Google Scholar
  11. 11.
    Lesire, C., Tessier, C.: Particle petri nets for aircraft procedure monitoring under uncertainty. In: ICATPN, pp. 329–348 (2005)Google Scholar
  12. 12.
    Kartson, D., Balbo, G., Donatelli, S., Franceschinis, G., Conte, G.: Modelling with Generalized Stochastic Petri Nets. John Wiley & Sons, Inc., New YorkGoogle Scholar
  13. 13.
    Murata, T.: Petri Nets: Properties, analysis and applications. Proceedings of the IEEE, 541–580Google Scholar
  14. 14.
    Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28 (1998)CrossRefGoogle Scholar
  15. 15.
    Arnaud, Doucet, Johansen: A tutorial on particle filtering and smoothing: Fifteen years later. Technical report (2008)Google Scholar
  16. 16.
    Nghiem, A.T., Bremond, F., Thonnat, M., Valentin, V.: ETISEO, performance evaluation for video surveillance systems. In: AVSS 2007, London, UK (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gal Lavee
    • 1
  • Michael Rudzsky
    • 1
  • Ehud Rivlin
    • 1
    • 2
  1. 1.Computer Science Department, TechnionIsrael Institute of TechnologyHaifaIsrael
  2. 2.Google Inc.Mountain ViewUSA

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