# 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…
42 Citations

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## References

SHOWING 1-10 OF 46 REFERENCES

### Multi-Target Tracking With Time-Varying Clutter Rate and Detection Profile: Application to Time-Lapse Cell Microscopy Sequences

• Computer Science
IEEE Transactions on Medical Imaging
• 2015
A bootstrap filter composed of an estimator and a tracker based on the random finite set Bayesian filtering framework that can outperform state-of-the-art particle trackers on both synthetic and real data in this regime.

### Estimating detection statistics within a Bayes-closed multi-object filter

• Computer Science, Mathematics
2016 19th International Conference on Information Fusion (FUSION)
• 2016
A Random Finite Set (RFS) based algorithm which is capable of estimating both the probability of detection and the clutter rate, while jointly estimating the multi-target state of the system is presented.

### Visual Tracking in Background Subtracted Image Sequences via Multi-Bernoulli Filtering

• Computer Science
IEEE Transactions on Signal Processing
• 2013
This correspondence presents a novel method for simultaneous tracking of multiple non-stationary targets in video, which proposes a multi-target likelihood function for the background-subtracted grey-scale image data, which admits multi- target conjugate priors.

### Multiobject Tracking for Generic Observation Model Using Labeled Random Finite Sets

• Computer Science
IEEE Transactions on Signal Processing
• 2018
This paper presents an exact Bayesian filtering solution for the multiobject tracking problem with the generic observation model, designed in the labeled random finite set framework, using the product styled representation of labeled multiobject densities.

### CPHD and PHD filters for unknown backgrounds, part III: tractable multitarget filtering in dynamic clutter

• Engineering, Mathematics
Defense + Commercial Sensing
• 2010
This paper describes an approach that avoids combinatorial sums and is therefore potentially computationally tractable in the CPHD/PHD filters capable of multitarget track-before-detect capability.

### Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

• Computer Science
IEEE Transactions on Signal Processing
• 2014
The present paper details efficient implementations of the δ-GLMB multi-target tracking filter and presents inexpensive look-ahead strategies to reduce the number of computations.

### Labeled Random Finite Sets and Multi-Object Conjugate Priors

• Mathematics, Computer Science
IEEE Transactions on Signal Processing
• 2013
A new class of RFS distributions is proposed that is conjugate with respect to the multiobject observation likelihood and closed under the Chapman-Kolmogorov equation and is tested on a Bayesian multi-target tracking algorithm.

### Joint Detection and Estimation of Multiple Objects From Image Observations

• Computer Science
IEEE Transactions on Signal Processing
• 2010
A multi-object filter suitable for image observations with low signal-to-noise ratio (SNR) is developed and a particle implementation of the multi- object filter is proposed and demonstrated via simulations.

### Background and foreground modeling using nonparametric kernel density estimation for visual surveillance

• Computer Science
Proc. IEEE
• 2002
This paper constructs a statistical representation of the scene background that supports sensitive detection of moving objects in the scene, but is robust to clutter arising out of natural scene variations.