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The authors 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. Their proposed Bayesian inference schemes, which are primarily based on (Markov chain) Monte Carlo sequential methods, include: an evolving network-based multiple object tracking algorithm that is capable of categorizing objects into groups, a multiple cluster tracking algorithm for dealing with prohibitively large number of objects, and a causality inference framework for identifying dominant agents based exclusively on their observed trajectories. They use these as building blocks for developing a unified tracking and behavioral reasoning paradigm.
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