Situation Recognition and Hypothesis Management Using Petri Nets

  • Anders Dahlbom
  • Lars Niklasson
  • Göran Falkman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5861)


Situation recognition – the task of tracking states and identifying situations – is a problem that is important to look into for aiding decision makers in achieving enhanced situation awareness. The purpose of situation recognition is, in contrast to producing more data and information, to aid decision makers in focusing on information that is important for them, i.e. to detect potentially interesting situations. In this paper we explore the applicability of a Petri net based approach for modeling and recognizing situations, as well as for managing the hypothesis space coupled to matching situation templates with the present stream of data.


Situation recognition information fusion petri nets hypothesis management multi-agent activity recognition situation assessment 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Steinberg, A., Bowman, C., White, F.: Revisions to the JDL Data Fusion Model. In: Sensor Fusion: Architectures, Algorithms, and Applications, Proceedings of the SPIE, vol. 3719 (1999)Google Scholar
  2. 2.
    Llinas, J., Bowman, C., Rogova, G., Steinberg, A., Waltz, E., White, F.: Revisiting the JDL Data Fusion Model II. In: Proceedings of the 7th International Conference on Information Fusion, Stockholm, Sweden (2004)Google Scholar
  3. 3.
    Lambert, D.: An Exegesis of Data Fusion. In: Reznik, L., Kreinovich, V. (eds.) Soft Computing in Measurement and Information Acquisition. Springer, Heidelberg (2003)Google Scholar
  4. 4.
    Toth, G., Kokar, M., Wallenius, K., Laskey, K., Sudit, M., Hultner, M., Kessler, O.: Higher-Level Information Fusion: Challenges to the Academic Community. In: Proceedings of the 11th International Conference on Information Fusion, Cologne, Germany (2008)Google Scholar
  5. 5.
    Endsley, M.: Theoretical Underpinnings of Situation Awareness: A Critical Review. In: Endsley, M., Garland, J. (eds.) Situation Awareness Analysis and Measurement, pp. 20–30. Lawrence Erlbaum Associates, Mahwah (2000)Google Scholar
  6. 6.
    Dahlbom, A., Niklasson, L., Falkman, G., Loutfi, A.: Towards Template-based Situation Recognition. In: Proceedings of SPIE Defense, Security, and Sensing, Orlando, FL, USA, vol. 7352a (2009)Google Scholar
  7. 7.
    Allen, J.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM 26(11), 832–843 (1983)zbMATHCrossRefGoogle Scholar
  8. 8.
    Mörchen, F.: Unsupervised pattern mining from symbolic temporal data. SIGKDD Explorations 9(1), 41–55Google Scholar
  9. 9.
    Castel, C., Chaudron, L., Tessier, C.: What is going on? A high level interpretation of sequences of images. In: Proceedings of the 4th European conference on computer vision, Workshop on conceptual descriptions from images, Cambridge, UK (1996)Google Scholar
  10. 10.
    Ghanem, N., DeMenthon, D., Doermann, D., Davis, L.: Representation and Recognition of Events in Surveillance Video Using Petri Nets. In: Proceedings Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2004), pp. 112–120 (2004)Google Scholar
  11. 11.
    Perše, M., Kristan, M., Perš, J., Kovačič, S.: Recognition of Multi-Agent Activities with Petri Nets. In: Proceedings of the 17th International Electrotechnical and Computer Science Conference, Portorož, Slovenia (2008)Google Scholar
  12. 12.
    Lavee, G., Borzin, A., Rivlin, E., Rudzsky, M.: Building Petri Nets from Video Event Ontologies. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Paragios, N., Tanveer, S.-M., Ju, T., Liu, Z., Coquillart, S., Cruz-Neira, C., Müller, T., Malzbender, T. (eds.) ISVC 2007, Part I. LNCS, vol. 4841, pp. 442–451. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Dousson, C., Gaborit, P., Ghallab, M.: Situation Recognition: Representation and Algorithms. In: Proceedings of the IJCAI (1993)Google Scholar
  14. 14.
    Meyer-Delius, D., Plagemann, C., von Wichert, G., Feiten, W., Lawitzky, G., Burgard, W.: A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems. In: Proceedings of the 31st Annual Conference of the German Classification Society and Analysis, Machine Learning, and Applications (2007)Google Scholar
  15. 15.
    Murata, T.: Petri Nets: Properties, Analysis and Applications. Proceedings of the IEEE 77(4), 541–580 (1989)CrossRefGoogle Scholar
  16. 16.
    Sowa, J.: Knowledge Representation: logical philosophical, and computational foundations. Brooks/Cole Thomson Learning, Pacifik Grove (2000)Google Scholar
  17. 17.
    Dahlbom, A., Niklasson, L., Falkman, G.: A Component-based Simulator for Supporting Research on Situation Recognition. In: Proceedings of SPIE Defense, Security, and Sensing, Orlando, FL, USA, vol. 7352a (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Anders Dahlbom
    • 1
  • Lars Niklasson
    • 1
  • Göran Falkman
    • 1
  1. 1.Informatics Research CentreUniversity of SkövdeSkövdeSweden

Personalised recommendations