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…
Figures and Tables from this paper
41 Citations
Multi-object tracking with an adaptive generalized labeled multi-Bernoulli filter
- Computer ScienceSignal Process.
- 2022
Tracking Multiple Marine Ships via Multiple Sensors with Unknown Backgrounds
- EngineeringSensors
- 2019
A method for online tracking multiple targets using multiple sensors which jointly adapts to the unknown clutter rate and the probability of detection and the validity of the proposed method is demonstrated via numerical study using multistatic Doppler data.
Multi-target tracking with an adaptive $\delta-$GLMB filter
- Computer Science
- 2020
A plug-and-play multi-target tracking algorithm based on the recent $\delta$-Generalized Labeled Multi-Bernoulli $-GLMB) filter which remove the guess work in determining the parameters of the target birth process, the detection probability, and clutter rate online is proposed.
A Bayesian 3D Multi-view Multi-object Tracking Filter
- Computer ScienceArXiv
- 2020
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
- Computer Science, EngineeringIEEE Access
- 2020
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.
Multi-object Tracking in Unknown Detection Probability with the PMBM Filter
- Computer ScienceArXiv
- 2019
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.
Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking
- Computer ScienceSensors
- 2022
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
- Engineering, MathematicsSensors
- 2019
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.
Robust Multi-Sensor Generalized Labeled Multi-Bernoulli Filter
- Engineering, Computer ScienceSignal Processing
- 2021
Robust Measurement-Driven Cardinality Balance Multi-Target Multi-Bernoulli Filter
- EngineeringSensors
- 2021
Numerical experiments have verified that the RMD-CBMeMBer filter can solve the multi-target tracking problem under the condition of unknown target detection probability, unknown background clutter density and inadequate prior position information of the target.
References
SHOWING 1-10 OF 56 REFERENCES
Multi-Target Tracking With Time-Varying Clutter Rate and Detection Profile: Application to Time-Lapse Cell Microscopy Sequences
- Computer ScienceIEEE 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, Mathematics2016 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 of numerous targets via multi-Bernoulli filtering of image data
- Computer SciencePattern Recognit.
- 2012
Multiobject Tracking for Generic Observation Model Using Labeled Random Finite Sets
- Computer ScienceIEEE 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.
Labeled Random Finite Sets and Multi-Object Conjugate Priors
- Mathematics, Computer ScienceIEEE 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.
Multi-Bernoulli sensor-selection for multi-target tracking with unknown clutter and detection profiles
- Computer ScienceSignal Process.
- 2016
Joint Detection and Estimation of Multiple Objects From Image Observations
- Computer ScienceIEEE 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 ScienceProc. 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.
CPHD Filtering With Unknown Clutter Rate and Detection Profile
- EngineeringIEEE Transactions on Signal Processing
- 2011
This paper devise versions of the PHD/CPHD filters that can adaptively learn the clutter rate and detection profile while filtering and derive closed-form solutions to these filtering recursions using Beta and Gaussian mixtures.
CPHD and PHD filters for unknown backgrounds I: dynamic data clustering
- EngineeringDefense + Commercial Sensing
- 2009
This paper derives the measurement-update equations for CPHD and PHD filters that estimate models of unknown, dynamically changing data, such as background clutter and generalizes these results to multitarget detection and tracking in unknown, dynamic clutter.