Tracking multiple cells by correspondence resolution in a sequential Bayesian framework
We propose a multi-target tracking (MTT) algorithm in a sequential Bayesian framework that computes cell velocities from video microscopy. Unlike the traditional tracking methods, our formulation does not involve the estimation of target states; instead, we estimate one-to-one target correspondences by way of a sequential Markov chain Monte Carlo (MCMC) algorithm. The proposed probabilistic framework also automatically accounts for a variable number of targets. We have tested the proposed tracking algorithm on two different in vitro and one in vivo microscopy experiments. The three experiments show that the method holds promise in terms of low false positive and false negative rates as well as low rates of correspondence error.