CUED Publications database

Tracking multiple moving objects in images using Markov Chain Monte Carlo

Jiang, L and Singh, SS Tracking multiple moving objects in images using Markov Chain Monte Carlo. Statistics and Computing. (Unpublished)

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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.

Item Type: Article
Uncontrolled Keywords: mutli-target tracking Markov Chain Monte Carlo particle Markov Chain Monte Carlo reversible jump single molecule fluorescence microscopy
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 18:59
Last Modified: 01 Oct 2019 03:54
DOI: 10.1007/s11222-017-9743-9