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    OSPA<sup>(2)</sup>: Using the OSPA metric to evaluate multi-target tracking performance

    Access Status
    Fulltext not available
    Authors
    Beard, Michael
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Beard, M. and Vo, B.T. and Vo, B. 2017. OSPA<sup>(2)</sup>: Using the OSPA metric to evaluate multi-target tracking performance, pp. 86-91.
    Source Title
    2017 International Conference on Control, Automation and Information Sciences, ICCAIS 2017
    DOI
    10.1109/ICCAIS.2017.8217598
    ISBN
    9781538631140
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/67528
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 IEEE. The optimal sub-pattern assignment (OSPA) metric is a distance between two sets of points that jointly accounts for the dissimilarity in the number of points and the values of the points in the respective sets. The OSPA metric is often used for measuring the distance between two sets of points in Euclidean space. A common example is in multi-target filtering, where the aim is to estimate the set of current target states, all of which have the same dimension. In multi-target tracking (MTT), the aim is to estimate the set of target tracks over a period of time, rather than the set of target states at each time step. In this case, it is not sufficient to analyse the multi-target filtering error at each time step in isolation. It is important that a metric for evaluating MTT performance accounts for the dissimilarity between the overall target tracks, which are generally of different dimensions. In this paper, we demonstrate that MTT error can be captured using the OSPA metric to define a distance between two sets of tracks.

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    • Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms
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      © 2018 ISIF The traditional method of applying the optimal subpattern assignment (OSPA) metric cannot fully evaluate multitarget tracking performance, as it does not account for phenomena such as track label switching, ...
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