Multiple object tracking in unknown backgrounds with labeled random finite sets

Punchihewa, Y, Vo, B, Vo, B and Kim, D 2018, 'Multiple object tracking in unknown backgrounds with labeled random finite sets', IEEE Transactions on Signal Processing, vol. 66, no. 11, pp. 3040-3055.

Document type: Journal Article
Collection: Journal Articles

Title Multiple object tracking in unknown backgrounds with labeled random finite sets
Author(s) Punchihewa, Y
Vo, B
Vo, B
Kim, D
Year 2018
Journal name IEEE Transactions on Signal Processing
Volume number 66
Issue number 11
Start page 3040
End page 3055
Total pages 16
Publisher Institute of Electrical and Electronics Engineers
Abstract 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 multiobject tracker, can be tailored to learn clutter and detection parameters on-the-fly while tracking. Provided that these background model parameters do not fluctuate rapidly compared to the data rate, the proposed algorithm can adapt to the unknown background yielding better tracking performance.
Subject Signal Processing
Keyword(s) Data association
Generalized labeled multi-Bernoulli
Gibbs sampling
Multi-object tracking
Optimal assignment
Random finite sets
Ranked assignment
DOI - identifier 10.1109/TSP.2018.2821650
Copyright notice © 1991-2012 IEEE.
ISSN 1053-587X
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Citation counts: TR Web of Science Citation Count  Cited 12 times in Thomson Reuters Web of Science Article | Citations
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