MCMC-Based Tracking and Identification of Leaders in Groups

Avishy Carmi 1 Lyudmila Mihaylova 2 François Septier 3, 4 Sze Kim Pang 5 Pini Gurfil 6 Simon J. Godsill 7
1 Department of Mechanical and Aerospace, Nanyang Technological University
2 SCC - School of Computing and Communications [Lancaster]
3 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
4 Institut TELECOM/TELECOM Lille1
5 DSO National Laboratories
6 Faculty of Aerospace Engineering
7 Signal Processing Group Dept of Engineering
Abstract : We present a novel framework for identifying and track- ing 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 shap- ing 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 pro- posed scheme in identifying actual leaders in swarms of in- teracting agents and moving crowds.
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INSTITUT-TELECOM | CNRS | LAGIS-SI | LAGIS | IMT-LILLE-DOUAI

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Avishy Carmi, Lyudmila Mihaylova, François Septier, Sze Kim Pang, Pini Gurfil, et al.. MCMC-Based Tracking and Identification of Leaders in Groups. IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Nov 2011, Barcelona, Spain. pp.112-119, ⟨10.1109/ICCVW.2011.6130232⟩. ⟨hal-00685921⟩

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