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Monte Carlo-Based Bayesian Group Object Tracking and Causal Reasoning

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Advances in Intelligent Signal Processing and Data Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 410))

Abstract

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.

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Correspondence to Avishy Y. Carmi .

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Carmi, A.Y., Mihaylova, L., Gning, A., Gurfil, P., Godsill, S.J. (2013). Monte Carlo-Based Bayesian Group Object Tracking and Causal Reasoning. In: Georgieva, P., Mihaylova, L., Jain, L. (eds) Advances in Intelligent Signal Processing and Data Mining. Studies in Computational Intelligence, vol 410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28696-4_2

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