Monte Carlo-Based Bayesian group object tracking and causal reasoning

Avishy Y. Carmi, Lyudmila Mihaylova, Amadou Gning, Pini Gurfil, Simon J. Godsill

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations


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.

Original languageEnglish
Title of host publicationAdvances in Intelligent Signal Processing and Data Mining
Subtitle of host publicationTheory and Applications
PublisherSpringer Verlag
Number of pages47
ISBN (Print)9783642286957
StatePublished - 1 Jan 2013
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence


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