The track-oriented multiple hypothesis tracker is currently the preferred method for tracking multiple targets in clutter with medium to high computational resources. This method maintains a structured representation of the track posterior distribution, which it repeatedly extends and optimizes over. This representation of the posterior admits probabilistic inference tasks beyond MAP estimation that have yet to be explored. To this end we formulate the posterior as a graphical model and show that belief propagation can be used to approximate the track marginals. These approximate marginals enable an online parameter estimation scheme that improves tracker performance in the presence of parameter misspecification.