Monte Carlo-Based Bayesian Group Object Tracking and Causal Reasoning

  • Avishy Y. Carmi
  • Lyudmila Mihaylova
  • Amadou Gning
  • Pini Gurfil
  • Simon J. Godsill
Part of the Studies in Computational Intelligence book series (SCI, volume 410)


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.


Probability Density Function Markov Chain Monte Carlo Object Tracking Sequential Monte Carlo Causal Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  • Avishy Y. Carmi
    • 1
  • Lyudmila Mihaylova
    • 2
  • Amadou Gning
    • 2
  • Pini Gurfil
    • 3
  • Simon J. Godsill
    • 4
  1. 1.Department of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.School of Computing and CommunicationsLancaster UniversityLancasterUnited Kingdom
  3. 3.Department of Aerospace EngineeringTechnion – Israel Institute of TechnologyHaifaIsrael
  4. 4.Department of EngineeringUniversity of CambridgeCambridgeUnited Kingdom

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