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
Chapter
Part of the The International Series in Video Computing book series (VICO, volume 11)

Abstract

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.

Keywords

Covariance 

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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

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