Multiobject tracking for generic observation model using labeled random finite sets

Li, S, Yi, W, Hoseinnezhad, R, Wang, B and Kong, L 2018, 'Multiobject tracking for generic observation model using labeled random finite sets', IEEE Transactions on Signal Processing, vol. 66, no. 2, pp. 368-383.

Document type: Journal Article
Collection: Journal Articles

Title Multiobject tracking for generic observation model using labeled random finite sets
Author(s) Li, S
Yi, W
Hoseinnezhad, R
Wang, B
Kong, L
Year 2018
Journal name IEEE Transactions on Signal Processing
Volume number 66
Issue number 2
Start page 368
End page 383
Total pages 16
Publisher IEEE
Abstract This paper presents an exact Bayesian filtering solution for the multiobject tracking problem with the generic observation model. The proposed solution is designed in the labeled random finite set framework, using the product styled representation of labeled multiobject densities, with the standard multiobject transition kernel and no particular simplifying assumptions on the multiobject likelihood. Computationally tractable solutions are also devised by applying a principled approximation involving the replacement of the full multiobject density with a labeled multi-Bernoulli density that minimizes the KullbackLeibler divergence and preserves the first-order moment. To achieve the fast performance, a dynamic-grouping-procedure-based implementation is presented with a step-by-step algorithm. The performance of the proposed filter and its tractable implementations are verified and compared with the state of the art in numerical experiments.
Subject Signal Processing
Keyword(s) Generic observation model.
Marked point process
Multiobject tracking
Random finite set
DOI - identifier 10.1109/TSP.2017.2764864
Copyright notice © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
ISSN 1053-587X
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 22 times in Thomson Reuters Web of Science Article | Citations
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