# 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}
}
• Published 6 June 2017
• Computer Science, Engineering
• IEEE Transactions on Signal Processing
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…
41 Citations

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