Statistics and Computing

, Volume 28, Issue 3, pp 495–510 | Cite as

Tracking multiple moving objects in images using Markov Chain Monte Carlo

  • Lan Jiang
  • Sumeetpal S. Singh


A new Bayesian state and parameter learning algorithm for multiple target tracking models with image observations are proposed. Specifically, a Markov chain Monte Carlo algorithm is designed to sample from the posterior distribution of the unknown time-varying number of targets, their birth, death times and states as well as the model parameters, which constitutes the complete solution to the specific tracking problem we consider. The conventional approach is to pre-process the images to extract point observations and then perform tracking, i.e. infer the target trajectories. We model the image generation process directly to avoid any potential loss of information when extracting point observations using a pre-processing step that is decoupled from the inference algorithm. Numerical examples show that our algorithm has improved tracking performance over commonly used techniques, for both synthetic examples and real florescent microscopy data, especially in the case of dim targets with overlapping illuminated regions.


Mutli-target tracking Markov Chain Monte Carlo Particle Markov Chain Monte Carlo Reversible jump Single molecule fluorescence microscopy 



We thank Kristina Ganzinger and Professor David Klenerman for providing the real data and the code in Weimann et al. (2013) for the comparisons in Sect.  4.2, and Sinan Yıldırım for his careful reading of this paper.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of EngineeringUniversity of CambridgeCambridgeUK

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