  This paper proposes an on-line multiple object tracking algorithm that can
operate in unknown background. In a majority of multiple object tracking
applications, model parameters for background processes such as clutter and
detection are unknown and vary with time, hence the ability of the algorithm to
adaptively learn the these parameters is essential in practice. In this work,
we detail how the Generalized Labeled Multi Bernouli (GLMB) filter a tractable
and provably Bayes optimal multi-object tracker can be tailored to learn
clutter and detection parameters on the fly while tracking. Provided that these
background model parameters do not fluctuate rapidly compared to the data rate,
the proposed algorithm can adapt to the unknown background yielding better
tracking performance.
