Relational Dynamic Bayesian Networks to Improve Multi-target Tracking

  • Cristina Manfredotti
  • Enza Messina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5807)


Tracking relations between moving objects is a big challenge for Computer Vision research. Relations can be useful to better understand the behaviors of the targets, and the prediction of trajectories can become more accurate. Moreover, they can be useful in a variety of situations like monitoring terrorist activities, anomaly detection, sport coaching, etc.

In this paper we propose a model based on Relational Dynamic Bayesian Networks (RDBNs), that uses first-order logic to model particular correlations between objects behaviors, and show that the performance of the prediction increases significantly. In our experiments we consider the problem of multi-target tracking on a highway where the behavior of targets is often correlated to the behavior of the targets near to them. We compare the performance of a Particle Filter that does not take into account relations between objects and the performance of a Particle Filter that makes inference over the proposed RDBN.

We show that our method can follow the targets path more closely than the standard methods, being able to better predict their behaviors while decreasing the complexity of the tracker task.


Markov Chain Monte Carlo Particle Filter Transition Model Data Association Dynamic Bayesian Network 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning Probabilistic Relational Models. In: IJCAI, pp. 1300–1309 (1999)Google Scholar
  2. 2.
    Murphy, K.: Dynamic Bayesian Networks: Representation, Inference and Learning. Computer Science Division, University of California, Berkeley (2002)Google Scholar
  3. 3.
    Reid, D.: An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control (1979)Google Scholar
  4. 4.
    Cox, I.J., Hingorani, S.L.: An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence (1996)Google Scholar
  5. 5.
    Oh, S., Russell, S., Sastry, S.: Markov chain Monte Carlo data association for general multiple-target tracking problems. In: 43rd IEEE Conference on Decision and Control, 2004. CDC (2004)Google Scholar
  6. 6.
    Milch, B., Russell, S.J.: General-Purpose MCMC Inference over Relational Structures. UAI (2006)Google Scholar
  7. 7.
    Blockeel, H., De Raedt, L.: Top-Down Induction of First-Order Logical Decision Trees. Artif. Intell. 101(1-2) (1998)Google Scholar
  8. 8.
    Foster, J.: Provost and Pedro Domingos: Tree Induction for Probability-Based Ranking. Machine Learning 52(3) (2003)Google Scholar
  9. 9.
    Doucet, A., Defreitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice (2001)Google Scholar
  10. 10.
    Arulampalam, S., Maskell, S., Gordon, N.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Cristina Manfredotti
    • 1
  • Enza Messina
    • 1
  1. 1.DISCoUniversità degli Studi Milano-BicoccaItaly

Personalised recommendations