Monte Carlo-based Bayesian group object tracking and causal reasoning Academic Article uri icon


  • We present algorithms for tracking and reasoning of local traits in the subsystem level based on the observed emergent behavior of multiple coordi-nated groups in potentially cluttered environments. Our proposed Bayesian infer-ence 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 al-gorithm for dealing with prohibitively large number of objects, and 3) a causality inference framework for identifying dominant agents based exclusively on their ob-served trajectories. We use these as building blocks for developing a unified tracking and behavioral reasoning paradigm. Both synthetic and realistic examples are pro-vided for demonstrating the derived concepts.

publication date

  • January 1, 2013