Abstract
In this article, the evidential network is combined with a temporal credal filter in order to incorporate the time information and describe the information propagation from a node to another one. Then we describe an application in convoy detection and propose a complex simulated scenario. The results are compared with those of our previous approach with Bayesian networks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Francisco (1988)
Johansson, F., Falkman, G.: Implementation and integration of a Bayesian Network for prediction of tactical intention into a ground target simulator. In: 9th International Conference on Information Fusion, July 2006, pp. 1–7 (2006)
Murphy, K., Mian, S.: Modelling gene expression data using dynamic Bayesian networks. tech. rep., Lawrence Berkeley National Laboratory (1999)
Simon, C., Weber, P., Levrat, E.: Bayesian networks and evidence theory to model complex systems reliability. Journal of Computers 2(1), 33 (2007)
Benavoli, A., Ristic, B., Farina, A., Oxenham, M., Chisci, L.: An application of evidential networks to threat assessment. IEEE Transactions on Aerospace and Electronic Systems 45, 620–639 (2009)
Xu, H., Smets, P.: Reasoning in evidential networks with conditional belief functions. International Journal of Approximate Reasoning 14(2-3), 155–185 (1996)
Smets, P.: Belief functions: The disjunctive ule of Combination and the Generalized Bayesian theorem. International Journal of Approximate Reasoning 9(1), 1–35 (1993)
Smets, P.: Data fusion in the transferable belief model. In: Proc. Third Int. Conf. Information Fusion, Citeseer, pp. 10–13 (2000)
Ramasso, E., Rombaut, M., Pellerin, D.: A temporal belief filter improving human action recognition in videos. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006, May 2006, vol. 2, p. II (2006)
Ramasso, E., Rombaut, M., Pellerin, D.: State filtering and change detection using TBM conflict Application to human action recognition in athletics videos. IEEE Transactions on Circuits and Systems for Video Technology 17(7), 944 (2007)
Pollard, E., Pannetier, B., Rombaut, M.: Convoy detection processing by using the hybrid algorithm (GMCPHD/VS-IMMC-MHT) and dynamic bayesian networks. In: 12th International Conference on Information Fusion (2009)
Yaghlane, B., Mellouli, K.: Inference in directed evidential networks based on the transferable belief model. International Journal of Approximate Reasoning 48(2), 399–418 (2008)
Pollard, E., Pannetier, B., Rombaut, M.: Hybrid Algorithms for Multitarget Tracking using MHT and GMCPHD. IEEE Transactions on Aerospace and Electronic Systems (to appear)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pollard, E., Rombaut, M., Pannetier, B. (2010). Bayesian Networks vs. Evidential Networks: An Application to Convoy Detection. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. IPMU 2010. Communications in Computer and Information Science, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14055-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-14055-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14054-9
Online ISBN: 978-3-642-14055-6
eBook Packages: Computer ScienceComputer Science (R0)