Inferring Leadership from Group Dynamics Using Markov Chain Monte Carlo Methods

Avishy Carmi, Lyudmila Mihaylova, François Septier, Sze Kim Pang, S.J. Godsill

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.
Original languageEnglish
Title of host publicationModeling, Simulation and Visual Analysis of Crowds
EditorsS. Ali, K. Nishino, D. Manocha, M. Shah
PublisherSpringer New York
Number of pages22
ISBN (Electronic)978-1-4614-8483-7
ISBN (Print)978-1-4614-8483-7
StatePublished - 2013

Publication series

NameThe International Series in Video Computing


Dive into the research topics of 'Inferring Leadership from Group Dynamics Using Markov Chain Monte Carlo Methods'. Together they form a unique fingerprint.

Cite this