  A new Bayesian state and parameter learning algorithm for multiple target
tracking (MTT) models with image observations is proposed. Specifically, a
Markov chain Monte Carlo algorithm is designed to sample from the posterior
distribution of the unknown number of targets, their birth and death times,
states and model parameters, which constitutes the complete solution to the
tracking problem. The conventional approach is to pre-process the images to
extract point observations and then perform tracking. We model the image
generation process directly to avoid potential loss of information when
extracting point observations. 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.

Applications;Computer Vision and Pattern Recognition;Computation