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Labeled random finite sets and the bayes multi-target tracking filter

Vo, Ba-Ngu, Vo, Ba-Tuong and Phung, Dinh 2014, Labeled random finite sets and the bayes multi-target tracking filter, IEEE transactions on signal processing, vol. 62, no. 24, pp. 6554-6567, doi: 10.1109/TSP.2014.2364014.

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Title Labeled random finite sets and the bayes multi-target tracking filter
Author(s) Vo, Ba-Ngu
Vo, Ba-Tuong
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Journal name IEEE transactions on signal processing
Volume number 62
Issue number 24
Start page 6554
End page 6567
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2014-12-15
ISSN 1053-587X
1941-0476
Keyword(s) Bayesian estimation
conjugate prior
marked point process
random finite set
target tracking
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
HYPOTHESIS DENSITY FILTER
MULTI-BERNOULLI FILTER
VISUAL TRACKING
ALGORITHM
ASSIGNMENT
IMAGE
IMPLEMENTATIONS
RANKING
TARGETS
ORDER
Summary An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,” IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3460-3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the δ-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the δ-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the L1-error in the multi-target density arising from the truncation is presented.
Language eng
DOI 10.1109/TSP.2014.2364014
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083595

Document type: Journal Article
Collection: Centre for Pattern Recognition and Data Analytics
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