Inferring Leadership from Group Dynamics Using Markov Chain Monte Carlo Methods

  • Avishy Y. Carmi
  • Lyudmila Mihaylova
  • François Septier
  • Sze Kim Pang
  • Pini Gurfil
  • Simon J. Godsill
Part of the The International Series in Video Computing book series (VICO, volume 11)


This chapter presents a novel framework for identifying and tracking dominant agents in groups. The proposed approach relies on a causality detection scheme that is capable of ranking agents with respect to their contribution in recognizing the system’s collective behavior based exclusively on the agents’ observed trajectories. Further, the reasoning paradigm is made robust to multiple emissions and clutter by employing a class of recently introduced Markov chain Monte Carlo-based group tracking methods. Examples are provided that demonstrate the strong potential of the proposed scheme in identifying actual leaders in swarms of interacting agents and moving crowds.


Markov Chain Monte Carlo Gaussian Mixture Model Actual Leader Probability Hypothesis Density Reversible Jump Markov Chain Monte Carlo 
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.


  1. 1.
    Albert, R., Barabsi, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47–97 (2002)CrossRefMATHGoogle Scholar
  2. 2.
    Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: Computer Vision – ECCV 2008. Volume 5303 of Lecture Notes in Computer Science, pp. 1–14. Springer, Berlin/Heidelberg (2008)Google Scholar
  3. 3.
    Angelova, D., Mihaylova, L.: Extended object tracking using Monte Carlo methods. IEEE Trans. Signal Process. 56(2), 825–832 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)CrossRefGoogle Scholar
  5. 5.
    Berzuini, C., Nicola, G., Gilks, W.R., Larizza, C.: Dynamic conditional independence models and Markov chain Monte Carlo methods. J. Am. Stat. Assoc. 92(440), 1403–1412 (1997)CrossRefGoogle Scholar
  6. 6.
    Bhaskar, H., Mihaylova, L.: Combined data association and evolving population particle filter for tracking of multiple articulated targets. EURASIP J. Image Video Process. 2011, article ID 642532 (2011)Google Scholar
  7. 7.
    Bhaskar, H., Mihaylova, L., Maskell, S.: Population-based particle filters. In: Proceedings of the from the Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications, Birmingham, pp. 31–38 (2008)Google Scholar
  8. 8.
    Cappé, O., Guillin, A., Marin, J.-M., Robert, C.P., Roberty, C.P.: Population Monte Carlo. J. Comput. Gr. Stat. 13, 907–929 (2004)CrossRefGoogle Scholar
  9. 9.
    Carmi, A., Septier, F., Godsill, S.J.: The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking. In: Proceedings of the 12th International Conference on Information Fusion, pp. 1179–1186. Seattle, WA (2009)Google Scholar
  10. 10.
    Cheng, J., Greiner, R., Kelly, J., Bell, D., Liu, W.: Learning Bayesian network from data: an information-theory based approach. Artif. Intell. 137(1–2), 43–90 (2002)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of networks. Adv. Phys. 51, 1079–1187 (2002)CrossRefGoogle Scholar
  12. 12.
    Geffner, H.: Default Reasoning: Causal and Conditional Theories. MIT, Cambridge (1992)Google Scholar
  13. 13.
    Geyer, C.: Markov chain maximum likelihood. In: Keramigas, E. (ed.) Computing Science and Statistics: The 23rd Symposium on the Interface. Interface Foundation, Fairfax (1991)Google Scholar
  14. 14.
    Geyer, C., Thompson, E.A.: Annealing Markov chain Monte Carlo with applications to ancestral inference. J. Am. Stat. Assoc. 90, 909–920 (1995)CrossRefMATHGoogle Scholar
  15. 15.
    Gning, A., Mihaylova, L., Maskell, S., Pang, S.K., Godsill, S.: Group object structure and state estimation with evolving networks and Monte Carlo methods. IEEE Trans. Signal Process. 12(2), 523–536 (2011)Google Scholar
  16. 16.
    Golyandina, N., Nekrutkin, V., Zhigljavsky, A. (eds.): Analysis of Time Series Structure: SSA and Related Techniques. Chapman and Hall, Boca Raton (2001)Google Scholar
  17. 17.
    Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969)CrossRefGoogle Scholar
  18. 18.
    Green, P.J.: Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82(4), 711–732 (1995)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Helbing, D.: Traffic and related self-driven many-particle systems. Rev. Mod. Phys. 73, 1067–1141 (2002)CrossRefGoogle Scholar
  20. 20.
    Holland, P.W.: Statistics and causal inference. J. Am. Stat. Assoc. 81, 945–960 (1986)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Iba, Y.: Population-based Monte Carlo algorithms. J. Comput. Gr. Stat. 13(4), 175–193 (2000)Google Scholar
  22. 22.
    Iba, Y.: Population Monte Carlo algorithms. Trans. Jpn. Soc. Artif. Intell. 16, 279 (2000)CrossRefGoogle Scholar
  23. 23.
    Jasra, A., Stehphens, D.A., Holmes, C.C.: Population-based reversible jump Markov chain Monte Carlo. Biometrica 94(4), 787–807 (2007)CrossRefMATHGoogle Scholar
  24. 24.
    Khan, Z., Balch, T., Dellaert, F.: MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1805–1819 (2005)CrossRefGoogle Scholar
  25. 25.
    Liu, J.S.: Monte Carlo Strategies in Sceintific Computing. Springer, New York (2001)Google Scholar
  26. 26.
    Mahler, R.: Statistical Multisource-Multitarget Information Fusion. Artech House, Boston (2007)Google Scholar
  27. 27.
    Mihaylova, L., Boel, R., Hegyi, A.: Freeway traffic estimation within recursive Bayesian framework. Automatica 43(2), 290–300 (2007)MathSciNetCrossRefMATHGoogle Scholar
  28. 28.
    Pang, S.K., Li, J., Godsill, S.J.: Detection and tracking of coordinated groups. IEEE Trans. Aerosp. Electron. Syst. 47(1), 472–502 (2011)CrossRefGoogle Scholar
  29. 29.
    Pantrigo, J., Sánchez, A., Gianikellis, K., Monteymayor, A.S.: Combining particle filter and population based metahuristics for visual articulated object tracking. Electron. Lett. Comput. Vis. Image Anal. 5(3), 68–83 (2005)Google Scholar
  30. 30.
    Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK (2000)Google Scholar
  31. 31.
    Reynolds, C.W.: Flocks, herds, and schools: a distributed behavioral model. Comput. Gr. 21, 25–34 (1987)CrossRefGoogle Scholar
  32. 32.
    Shoam, Y.: Reasoning About Change: Time and Causation from the Standpoint of Artificial Intelligence. MIT, Cambridge (1988)Google Scholar
  33. 33.
    Strens, M.: Evolutionary MCMC sampling and optimization in discrete spaces. In: Proceedings of the Twentieth International Conference on Machine Learning, Washington, DC (2003)Google Scholar
  34. 34.
    Vo, B., Singh, S., Doucet, A.: Sequential Monte Carlo methods for multi-target filtering with random finite sets. IEEE Trans. Aerosp. Electron. Syst. 41(4), 1224–1245 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Avishy Y. Carmi
    • 1
  • Lyudmila Mihaylova
    • 2
  • François Septier
    • 3
  • Sze Kim Pang
    • 4
  • Pini Gurfil
    • 5
  • Simon J. Godsill
    • 6
  1. 1.Department of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.School of Computing and CommunicationsLancaster UniversityLancasterUK
  3. 3.Signal Processing and Information Theory Group, TELECOM Lille 1Villeneuve d’Ascq CedexFrance
  4. 4.DSO National LaboratoriesSingaporeSingapore
  5. 5.Department of Aerospace EngineeringTechnion Israel Institute of TechnologyHaifaIsrael
  6. 6.Department of EngineeringUniversity of CambridgeCambridgeUK

Personalised recommendations