Most multiobject tracking methods rely on a statistical model that involves unknown parameters. Here, we propose a Bayesian method for class-aided online learning of model parameters within extended multiobject tracking. We address the case where the extended objects belong to unknown object classes defined by unknown values of the model parameters. The proposed method learns the number of object classes, the class parameters, and the objects’ class affiliations simultaneously with the tracking process, and the learned class and parameter information is leveraged for improved tracking. This is enabled by a parameter-dependent state-space model for extended multiobject tracking that incorporates a Dirichlet process prior, and by a related Gibbs sampler for online learning. Our simulation results demonstrate substantial gains in tracking performance due to class-aided online parameter learning.