CUED Publications database

Monte Carlo-Based Bayesian group object tracking and causal reasoning

Carmi, AY and Mihaylova, L and Gning, A and Gurfil, P and Godsill, SJ (2013) Monte Carlo-Based Bayesian group object tracking and causal reasoning. Studies in Computational Intelligence, 410. pp. 7-53. ISSN 1860-949X

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We present algorithms for tracking and reasoning of local traits in the subsystem level based on the observed emergent behavior of multiple coordinated groups in potentially cluttered environments. Our proposed Bayesian inference schemes, which are primarily based on (Markov chain) Monte Carlo sequential methods, include: 1) an evolving network-based multiple object tracking algorithm that is capable of categorizing objects into groups, 2) a multiple cluster tracking algorithm for dealing with prohibitively large number of objects, and 3) a causality inference framework for identifying dominant agents based exclusively on their observed trajectories.We use these as building blocks for developing a unified tracking and behavioral reasoning paradigm. Both synthetic and realistic examples are provided for demonstrating the derived concepts. © 2013 Springer-Verlag Berlin Heidelberg.

Item Type: Article
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:30
Last Modified: 22 Oct 2019 08:24
DOI: 10.1007/978-3-642-28696-4_2