A Bayesian look at the optimal track labelling problem
Paper i proceeding, 2012

In multi-target tracking (MTT), the problem of assigning labels to tracks (track labelling) is vastly covered in literature, but its exact mathematical formulation, in terms of Bayesian statistics, has not been yet looked at in detail. Doing so, however, may help us to understand how Bayes-optimal track labelling should be performed or numerically approximated. Moreover, it can help us to better understand and tackle some practical difficulties associated with the MTT problem, in particular the so-called "mixed labelling" phenomenon that has been observed in MTT algorithms. In this paper, we rigorously formulate the optimal track labelling problem using Finite Set Statistics (FISST), and look in detail at the mixed labeling phenomenon. As practical contributions of the paper, we derive a new track extraction formulation with some nice properties and a statistic associated with track labelling with clear physical meaning. Additionally, we show how to calculate this statistic for two well-known MTT algorithms.

Track labelling

Target tracking

Finite Set Statistics

Particle filter


E. H. Aoki

University of Twente

Y. Boers

Thales Nederland B.V.

Lennart Svensson

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

P. Mandal

University of Twente

A. Bagchi

University of Twente

9th IET Data Fusion and Target Tracking Conference: Algorithms and Applications, DF and TT 2012, London,16-17 May 2012



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