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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42 Citations
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42 Citations

Multi-object tracking with an adaptive generalized labeled multi-Bernoulli filter

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The key innovation is a high fidelity yet tractable 3D occlusion model, amenable to optimal Bayesian multi-view multi-object filtering, which seamlessly integrates, into a single Bayesian recursion, the sub-tasks of track management, state estimation, clutter rejection, and occlusions/misdetection handling.

A Generalized Labelled Multi-Bernoulli Filter for Extended Targets With Unknown Clutter Rate and Detection Profile

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A multiple extended target tracking algorithm based on the generalized labelled multi-Bernoulli filter under the circumstance of unknown clutter rate and detection profile is proposed in this article.

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TLDR
This paper details how the detection probability can be estimated accompanied with the state estimates and closed-form solutions to the proposed method are derived by approximating the intensity of Poisson random finite set (RFS) to a Beta-Gaussian mixture form and density of Bernoulli RFS to a single Beta- Gaussian form.

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The paper adopts the dependent Dirichlet process (DDP) to learn the multiple object state prior by exploiting inherent dynamic dependencies in the state transition using the dynamic clustering property of the DDP.

Tracking Multiple Targets from Multistatic Doppler Radar with Unknown Probability of Detection

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A closed form labeled multitarget Bayes filter was used to track unknown and time-varying targets with unknown probability of detection in the presence of clutter, misdetection, and association uncertainty.

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