Multiple Object Tracking in Unknown Backgrounds With Labeled Random Finite Sets

@article{Punchihewa2018MultipleOT,
  title={Multiple Object Tracking in Unknown Backgrounds With Labeled Random Finite Sets},
  author={Yuthika Punchihewa and Ba-Tuong Vo and Ba-Ngu Vo and Du Yong Kim},
  journal={IEEE Transactions on Signal Processing},
  year={2018},
  volume={66},
  pages={3040-3055}
}
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… 

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