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MCMC-Based Tracking and Identification of Leaders in Groups

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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MCMC-Based Tracking and Identification of Leaders in Groups. / Carmi, Avishy; Mihaylova, Lyudmila; Septier, Francois et al.
IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011 . IEEE, 2011. p. 112-119.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Carmi, A, Mihaylova, L, Septier, F, Pang, SK & Godsill, S 2011, MCMC-Based Tracking and Identification of Leaders in Groups. in IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011 . IEEE, pp. 112-119, EEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds, Barcelona, Spain, 6/11/11. https://doi.org/10.1109/ICCVW.2011.6130232

APA

Carmi, A., Mihaylova, L., Septier, F., Pang, S. K., & Godsill, S. (2011). MCMC-Based Tracking and Identification of Leaders in Groups. In IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011 (pp. 112-119). IEEE. https://doi.org/10.1109/ICCVW.2011.6130232

Vancouver

Carmi A, Mihaylova L, Septier F, Pang SK, Godsill S. MCMC-Based Tracking and Identification of Leaders in Groups. In IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011 . IEEE. 2011. p. 112-119 doi: 10.1109/ICCVW.2011.6130232

Author

Carmi, Avishy ; Mihaylova, Lyudmila ; Septier, Francois et al. / MCMC-Based Tracking and Identification of Leaders in Groups. IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011 . IEEE, 2011. pp. 112-119

Bibtex

@inproceedings{db98a9ca1edd477fa02754999d996a61,
title = "MCMC-Based Tracking and Identification of Leaders in Groups",
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{\textquoteright}s collective behaviour based exclusively on the agents{\textquoteright} 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.",
keywords = "MCMC, tracking, crowd behaviour, particle filtering, causality",
author = "Avishy Carmi and Lyudmila Mihaylova and Francois Septier and Pang, {S. K.} and Simon Godsill",
year = "2011",
month = nov,
day = "7",
doi = "10.1109/ICCVW.2011.6130232",
language = "English",
isbn = "978-1-4673-0062-9",
pages = "112--119",
booktitle = "IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011",
publisher = "IEEE",
note = "EEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds ; Conference date: 06-11-2011 Through 13-11-2011",

}

RIS

TY - GEN

T1 - MCMC-Based Tracking and Identification of Leaders in Groups

AU - Carmi, Avishy

AU - Mihaylova, Lyudmila

AU - Septier, Francois

AU - Pang, S. K.

AU - Godsill, Simon

PY - 2011/11/7

Y1 - 2011/11/7

N2 - 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.

AB - 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.

KW - MCMC

KW - tracking

KW - crowd behaviour

KW - particle filtering

KW - causality

U2 - 10.1109/ICCVW.2011.6130232

DO - 10.1109/ICCVW.2011.6130232

M3 - Conference contribution/Paper

SN - 978-1-4673-0062-9

SP - 112

EP - 119

BT - IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011

PB - IEEE

T2 - EEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds

Y2 - 6 November 2011 through 13 November 2011

ER -