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    Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms

    Access Status
    Fulltext not available
    Authors
    Beard, Michael
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Date
    2018
    Type
    Conference Paper
    
    Metadata
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    Citation
    Beard, M. and Vo, B.T. and Vo, B. 2018. Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms, pp. 1575-1581.
    Source Title
    2018 21st International Conference on Information Fusion, FUSION 2018
    DOI
    10.23919/ICIF.2018.8455700
    ISBN
    9780996452762
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/73225
    Collection
    • Curtin Research Publications
    Abstract

    © 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, and track fragmentation. The OSPA(2)has been proposed as a technique for applying the OSPA distance in a way that captures these effects, while retaining the properties of a true metric. In this paper, we demonstrate the behaviour of the OSPA(2)on some numerical examples, discuss some of its advantages and limitations, and show that it is capable of being applied to performance evaluation of large-scale scenarios in the order of a thousand targets.

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