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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [21 references]  Display  Hide  Download
https://hal-imt.archives-ouvertes.fr/hal-00685921
Contributor : François Septier <>
Submitted on : Friday, April 6, 2012 - 1:03:44 PM
Last modification on : Thursday, October 17, 2019 - 12:35:47 PM
Long-term archiving on : Saturday, July 7, 2012 - 2:35:09 AM

File

main_Final.pdf
Files produced by the author(s)

Identifiers

Collections

INSTITUT-TELECOM | LAGIS-SI | LAGIS | IMT-LILLE-DOUAI

Citation

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⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

318

Files downloads

227

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD