Carmi, Avishy and Mihaylova, Lyudmila and Septier, Francois and Pang, S. K. and Godsill, Simon (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. ISBN 978-1-4673-0062-9Full text not available from this repository.
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.
|Item Type:||Contribution in Book/Report/Proceedings|
|Uncontrolled Keywords:||MCMC ; tracking ; crowd behaviour ; particle filtering ; causality|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||24 Jan 2012 10:21|
|Last Modified:||17 Jan 2017 02:49|
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