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Modeling, Simulation and Visual Analysis of Crowds pp 325–346Cite as

Inferring Leadership from Group Dynamics Using Markov Chain Monte Carlo Methods

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Part of the book series: The International Series in Video Computing ((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.

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Author information

Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Avishy Y. Carmi

  2. School of Computing and Communications, Lancaster University, Lancaster, UK

    Lyudmila Mihaylova

  3. Signal Processing and Information Theory Group, TELECOM Lille 1, Villeneuve d’Ascq Cedex, France

    François Septier

  4. DSO National Laboratories, Singapore, Singapore

    Sze Kim Pang

  5. Department of Aerospace Engineering, Technion Israel Institute of Technology, Haifa, Israel

    Pini Gurfil

  6. Department of Engineering, University of Cambridge, Cambridge, UK

    Simon J. Godsill

Authors
  1. Avishy Y. Carmi
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  2. Lyudmila Mihaylova
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  3. François Septier
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  4. Sze Kim Pang
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  5. Pini Gurfil
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  6. Simon J. Godsill
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Corresponding author

Correspondence to Avishy Y. Carmi .

Editor information

Editors and Affiliations

  1. Center for Vision Technologies, SRI International, Princeton, New Jersey, USA

    Saad Ali

  2. Department of Computer Science, Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  3. Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA

    Dinesh Manocha

  4. Center for Research in Computer Vision, University of Central Florida, Orlando, Florida, USA

    Mubarak Shah

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Carmi, A.Y., Mihaylova, L., Septier, F., Pang, S.K., Gurfil, P., Godsill, S.J. (2013). Inferring Leadership from Group Dynamics Using Markov Chain Monte Carlo Methods. In: Ali, S., Nishino, K., Manocha, D., Shah, M. (eds) Modeling, Simulation and Visual Analysis of Crowds. The International Series in Video Computing, vol 11. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8483-7_13

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  • DOI: https://doi.org/10.1007/978-1-4614-8483-7_13

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-8482-0

  • Online ISBN: 978-1-4614-8483-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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