Temporal Bayesian Networks for Scenario Recognition

  • Ahmed Ziani
  • Cina Motamed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


This work presents an automatic scenario recognition system for video sequence interpretation. The recognition algorithm is based on a Bayesian Networks approach. The model of scenario contains two main layers. The first one enables to highlight atemporal events from the observed visual features. The second layer is focused on the temporal reasoning stage. The temporal layer integrates an event based approach in the framework of the Bayesian Networks. The temporal Bayesian network tracks lifespan of relevant events highlighted from the first layer. Then it estimates qualitative and quantitative relations between temporal events helpful for the recognition task. The global recognition algorithm is illustrated over real indoor images sequences for an abandoned baggage scenario.


Visual-surveillance scenario recognition image sequence analysis Bayesian Network 


  1. 1.
    Buxton, H.: Learning and Understanding Dynamic Scene Activity: A Review. Image and Vision Computing 21, 125–136 (2003)CrossRefGoogle Scholar
  2. 2.
    Motamed, C.: Motion detection and tracking using belief indicators for an automatic visual- surveillance system. Image and Vision Computing 24(11), 1192–1201 (2006)CrossRefGoogle Scholar
  3. 3.
    Castel, C., Chaudron, L., Tessier, C.: What Is Going On? A High Level Interpretation of Sequences of Images. In: 4th European conference on computer vision, Workshop on conceptual descriptions from images, Cambridge UK (1996)Google Scholar
  4. 4.
    Oliver, N., Rosario, B., Pentland, A.: A Bayesian Computer Vision System for Modeling Human Interactions. In: Christensen, H.I. (ed.) ICVS 1999. LNCS, vol. 1542, pp. 255–272. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Remagnino, P., Tan, T., Baker, K.: Agent orientated annotation in model based visual surveillance. In: Proceedings of the International Conference on Computer Vision, pp. 857–862 (1998)Google Scholar
  6. 6.
    Eude, V., Bouchon-Meunier, B., Collain, E.: Reconnaissance d’activités à l’aide de graphes temporels flous. In: Proc. LFA’97 Logique Floue et Applications, Lyon, France, pp. 91–98 (1997)Google Scholar
  7. 7.
    Starner, T., Pentland, A.: Real-time American Sign Language Recognition from Video Using Hidden Markov Models. In: Proceedings of International Symposium on Computer Vision, pp. 265–270 (1995)Google Scholar
  8. 8.
    Pearl, J.: Probabilistic reasoning in Intelligent Systems. Morgan Kaufman, San Francisco (1988)Google Scholar
  9. 9.
    Intille, S.S., Bobick, A.F.: Visual Recognition of multi-agent Action using Binary Temporal Relations. In: IEEE Proceedings of Computer Vision and Pattern Recognition, Fort Collins, CO (1999)Google Scholar
  10. 10.
    Hongeng, S., Nevatia, R.: Multi-Agent Event Recognition. In: ICCV’01, pp. 84–93 (2001)Google Scholar
  11. 11.
    Moënne-Loccoz, N., Brémond, F., Thonnat, M.: Recurrent Bayesian Network for the Recognition of Human Behaviors from Video. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds.) ICVS 2003. LNCS, vol. 2626, pp. 68–77. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Dean, T., Kanazawa, K.: A model for reasoning about persistence and causation. Computational Intelligence 5(3), 142–150 (1989)CrossRefGoogle Scholar
  13. 13.
    Nicholson, A.E., Brady, M.: The data association problem when monitoring robot vehicles using dynamic belief networks. In: ECAI 92: 10th European Conference on Artificial Intelligence (1992)Google Scholar
  14. 14.
    Arroyo-Figueroa, G., Sucar, L.E.: A Temporal Bayesian Network for Diagnosis and Prediction. Uncertainty in artificial intelligence. In: Proc. 15th Conf. Uncertainty Artif. Intell., pp. 13–20 (1999)Google Scholar
  15. 15.
    Galan, S.F., Dıez, F.J.: Modeling dynamic causal interactions with bayesian networks: temporal noisy gates. In: CaNew’, the 2nd International Workshop on Causal Networks held in conjunction with ECAI 2000, Berlin, Germany, August 2000, pp. 1–5 (2000)Google Scholar
  16. 16.
    Allen, J.F.: An interval based representation of temporal knowledge. In: International Joint Conference on Artificial Intelligence, pp. 221–226 (1981)Google Scholar
  17. 17.
    Koller, D., Pfeiffer, A.: Object-oriented Bayesian networks. In: Koller, D., Pfeiffer, A. (eds.) Uncertainty in Artificial Intelligence: Proceedings of the Thirteenth Conference (UAI-1997), pp. 302–313. Morgan Kaufmann Publishers, San Francisco (1997)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Ahmed Ziani
    • 1
  • Cina Motamed
    • 1
  1. 1.Laboratoire LASL EA 2600, Université du Littoral Côte d’Opale, Bat 2, 50 Rue F.Buisson, 62228 CalaisFrance

Personalised recommendations