Analytic Combinatorics for Multiple Object Tracking
Roy L. Streit,Robert Blair Angle,Murat Efe +2 more
- 01 Jan 2021
TL;DR: The method of analytic combinatorics (AC) is a unified approach to multiple object tracking that encodes joint probability distributions into probability generating functionals (PGFLs).
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Abstract: The method of analytic combinatorics (AC) is a unified approach to multiple object tracking that encodes joint probability distributions into probability generating functionals (PGFLs). PGFLs characterize distributions exactly. A high level view of the tracking applications of PGFLs is outlined in this paper. Assignment models in well-known filters are modeled as products of PGFLs. MHT and multiBernoulli PGFLs are compared. Track extraction and the “notched” filter of the (reduced) Palm process are discussed. Bounded complexity approximate particle filter weights are found by saddle point methods applied to the Cauchy integral form of the derivatives.
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Citations
An Uncertainty-Aware Performance Measure for Multi-Object Tracking
TL;DR: This work proposes the use of the negative log-likelihood (NLL) of the multi-object posterior given the set of groundtruth objects as a performance measure, which takes into account all available uncertainty information in a sound mathematical manner without hyperparameters.
Multiple Target Tracking With Unresolved Measurements
TL;DR: A multiple target tracking filter is developed for merged measurement problems that arise with finite resolution sensors and is incorporated directly in the joint likelihood function using analytic combinatorics techniques.
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A Low Computational Complexity JPDA Filter With Superposition
TL;DR: A low computational complexity Bayesian multiple target tracking filter, based on target superposition, is presented, which is applied to the well-known Joint Probabilistic Data Association filter to derive the JPDA with superposition (JPDAS) filter.
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Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation
Yuxuan Xia,Ángel F. García‐Fernández,Florian Meyer,Jason L. Williams,Karl Granström,Lennart Svensson +5 more
TL;DR: A PMB filter for EOT directly estimates the set of object trajectories using BP, leveraging PMBM conjugacy and factor graph representation.
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Labeled Random Finite Sets and Multi-Object Conjugate Priors
Ba-Tuong Vo,Ba-Ngu Vo +1 more
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Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter
Ba-Ngu Vo,Ba-Tuong Vo,Dinh Phung +2 more
TL;DR: 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.
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