A Fuzzy Spatio-temporal-Based Approach for Activity Recognition

  • Jean-Marie Le Yaouanc
  • Jean-Philippe Poli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7518)

Abstract

Over the last decade, there has been a significant deployment of systems dedicated to surveillance. These systems make use of real-time sensors that generate continuous streams of data. Despite their success in many cases, the increased number of sensors leads to a cognitive overload for the operator in charge of their analysis. However, the context and the application requires an ability to react in real-time. The research presented in this paper introduces a spatio-temporal-based approach the objective of which is to provide a qualitative interpretation of the behavior of an entity (e.g., a human or vehicle). The process is formally supported by a fuzzy logic-based approach, and designed in order to be as generic as possible.

Keywords

Spatio-temporal data modeling Automatic activity recognition Semantic trajectories Fuzzy logic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Allen, J.F., Ferguson, G.: Actions and events in interval temporal logic. Journal of Logic and Computation 4(5), 531–579 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Shet, V., Harwood, D., Davis, L.: VidMAP: Video monitoring of activity with Prolog. In: IEEE International Conference on Advanced Video and Signal based Surveillance, Como, Italy, pp. 224–229. IEEE Computer Society (2005)Google Scholar
  3. 3.
    Geerinck, T., Enescu, V., Ravyse, I., Sahli, H.: Rule-based video interpretation framework: Application to automated surveillance. In: Proceedings of the 5th International Conference on Image and Graphics, pp. 341–348. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  4. 4.
    Krausz, B., Herpers, R.: Metrosurv: Detecting events in subway stations. Multimedia Tools and Applications 50(1), 123–147 (2010)CrossRefGoogle Scholar
  5. 5.
    Ghanem, N., Dementhon, D., Doermann, D., Davis, L.: Representation and recognition of events in surveillance video using petri nets. In: Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society, Washington, DC (2004)Google Scholar
  6. 6.
    Bremond, F., Medioni, G.: Scenario recognition in airborne video imagery. In: Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Santa Barbara, CA, USA. IEEE Computer Society (1998)Google Scholar
  7. 7.
    Van de Weghe, N., Cohn, A., Maeyer, P., Witlox, F.: Representing moving objects in computer-based expert systems: the overtake event example. Expert Systems with Applications 29, 977–983 (2005)CrossRefGoogle Scholar
  8. 8.
    Noyon, V., Claramunt, C., Devogele, T.: A relative representation of trajectories in geographical spaces. GeoInformatica 11(4), 479–496 (2007)CrossRefGoogle Scholar
  9. 9.
    Gottfried, B.: Interpreting motion events of pairs of moving objects. GeoInformatica 15(2), 247–271 (2011)CrossRefGoogle Scholar
  10. 10.
    Erwig, M.: Toward spatiotemporal patterns. Spatio-Temporal Databases 1, 29–54 (2004)Google Scholar
  11. 11.
    Hornsby, K.S., King, K.: Modeling motion relations for moving objects on road networks. GeoInformatica 12(4), 477–495 (2008)CrossRefGoogle Scholar
  12. 12.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Arens, M., Nagel, H.-H.: Behavioral Knowledge Representation for the Understanding and Creation of Video Sequences. In: Günter, A., Kruse, R., Neumann, B. (eds.) KI 2003. LNCS (LNAI), vol. 2821, pp. 149–163. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    Lynch, K.: The Image of the City. The MIT Press, Boston (1960)Google Scholar
  15. 15.
    Smith, B., Varzi, A.: Fiat and Bona Fide Boundaries: Towards an Ontology of Spatially Extended Objects. In: Frank, A.U. (ed.) COSIT 1997. LNCS, vol. 1329, pp. 103–119. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  16. 16.
    Zhan, F.B.: Approximate analysis of binary topological relations between geographic regions with indeterminate boundaries. Soft Computing - A Fusion of Foundations, Methodologies and Applications 2, 28–34 (1998)Google Scholar
  17. 17.
    Hudelot, C., Atif, J., Bloch, I.: Fuzzy spatial relation ontology for image interpretation. Fuzzy Sets Systems 159(15), 1929–1951 (2008)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Clementini, E., Felice, P.D.: Approximate topological relations. International Journal of Approximate Reasoning 16(2), 173–204 (1997)MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Cohn, A.G., Gotts, N.M.: The ‘egg-yolk’ representation of regions with indeterminate boundaries. In: Burrough, P., Frank, A.M. (eds.) Specialist Meeting on Spatial Objects with Undetermined Boundaries, pp. 171–187. Taylor & Francis (1997)Google Scholar
  20. 20.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Communication of the ACM 26(11), 832–843 (1983)MATHCrossRefGoogle Scholar
  21. 21.
    Schockaert, S., Cock, M.D., Kerre, E.E.: Fuzzifying Allen’s temporal interval relations. IEEE Transactions on Fuzzy Systems 16(2), 517–533 (2008)CrossRefGoogle Scholar
  22. 22.
    Cariǹena, P., Bugarin, A., Mucientes, M., Barro, S.: A language for expressing fuzzy temporal rules. Mathware & Soft Computing 7, 213–227 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jean-Marie Le Yaouanc
    • 1
  • Jean-Philippe Poli
    • 1
  1. 1.CEA, LISTGif-sur-Yvette CedexFrance

Personalised recommendations