Combinatorial resampling particle filter: an effective and efficient method for articulated object tracking

DOI10.1007/S11263-014-0763-ZzbMath1398.68580OpenAlexW2037057738MaRDI QIDQ1799943

Séverine Dubuisson, Christophe Gonzales

Publication date: 19 October 2018

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-014-0763-z

zbMATH Keywords

resamplingdynamic Bayesian networksparticle filterarticulated object tracking

Mathematics Subject Classification ID

Machine vision and scene understanding (68T45)

Uses Software

• HumanEva

Cites Work

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• Predicting articulated human motion from spatial processes
• 3D human motion tracking based on a progressive particle filter
• A probabilistic exclusion principle for tracking multiple objects
• Sequential Monte Carlo Methods in Practice
• Constructing Free-Energy Approximations and Generalized Belief Propagation Algorithms
• Sequential Monte Carlo Methods for Dynamic Systems

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