Multiple Object Tracking in Unknown Backgrounds With Labeled Random Finite Sets

Abstract

This paper proposes an online multiple object tracker that can operate under unknown detection profile and clutter rate. 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 these parameters is essential in practice. In this paper, we detail how the generalized labeled multibernoulli filter, a tractable and provably Bayes optimal multiobject 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.