MCMC-based tracking and identification of leaders in groups

Avishy Y. Carmi, Lyudmila Mihaylova, François Septier, Sze Kim Pang, Pini Gurfil, Simon J. Godsill

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

We present a novel framework for identifying and tracking dominant agents in groups. Our proposed approach relies on a causality detection scheme that is capable of ranking agents with respect to their contribution in shaping the system's collective behaviour 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 publication2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
Pages112-119
Number of pages8
DOIs
StatePublished - 1 Dec 2011
Externally publishedYes
Event2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 - Barcelona, Spain
Duration: 6 Nov 201113 Nov 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
Country/TerritorySpain
CityBarcelona
Period6/11/1113/11/11

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