A Bayesian solution to multi-target tracking problems with mixed labelling

E.H. Aoki, Y. Boers, Lennart Svensson, Pranab K. Mandal, Arunabha Bagchi

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    Abstract

    In Multi-Target Tracking (MTT), the problem of assigning labels to tracks (track labelling) is vastly covered in literature and has been previously formulated using Bayesian recursion. However, the existing literature lacks an appropriate measure of uncertainty related to the assigned labels which has sound mathematical basis and clear practical meaning (to the user). This is especially important in a situation where targets move in close proximity with each other and thereafter separate again. Because, in such a situation it is well-known that there will be confusion on target identities, also known as “mixed labelling‿. In this paper, we provide a mathematical characterization of the labelling uncertainties present in Bayesian multi-target tracking and labelling (MTTL) problems and define measures of labelling uncertainties with clear physical interpretation. The introduced uncertainty measures can be used to find the optimal track label assignment, and evaluate track labelling performance. We also analyze in details the mixed labelling phenomenon in the presence of two targets. In addition, we propose a new Sequential Monte Carlo (SMC) algorithm, the Labelling Uncertainty Aware Particle Filter (LUA-PF), for the multi target tracking and labelling problem that can provide good estimates of the uncertainty measures. We validate this using simulation and show that the proposed method performs much better when compared with the performance of the SIR multi-target SMC filter.
    Original languageUndefined
    Place of PublicationEnschede
    PublisherUniversity of Twente, Department of Applied Mathematics
    Number of pages22
    Publication statusPublished - Jul 2014

    Publication series

    NameMemorandum of the Department of Applied Mathematics
    No.2036
    ISSN (Print)1874-4850

    Keywords

    • Labelling error
    • EWI-24915
    • Multi-target tracking
    • IR-91442
    • METIS-305948
    • Track labelling
    • Sequential Monte Carlo methods

    Cite this

    Aoki, E. H., Boers, Y., Svensson, L., Mandal, P. K., & Bagchi, A. (2014). A Bayesian solution to multi-target tracking problems with mixed labelling. (Memorandum of the Department of Applied Mathematics; No. 2036). Enschede: University of Twente, Department of Applied Mathematics.