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    Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning

    96261.pdf (4.822Mb)
    Access Status
    Open access
    Authors
    Lee, Ji Youn
    Shim, Changbeom
    Nguyen, Hoa Van
    Nguyen, Tran Thien Dat
    Choi, H.
    Kim, Y.
    Date
    2023
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Lee, J.Y. and Shim, C. and Nguyen, H.V. and Nguyen, T.T.D. and Choi, H. and Kim, Y. 2023. Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning. In Proceedings of 2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS), 27-29 Nov 2023, Hanoi, Vietnam.
    Source Title
    Proceedings - 12th IEEE International Conference on Control, Automation and Information Sciences, ICCAIS 2023
    Source Conference
    2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS)
    DOI
    10.1109/ICCAIS59597.2023.10382268
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/LP200301507
    URI
    http://hdl.handle.net/20.500.11937/96497
    Collection
    • Curtin Research Publications
    Abstract

    Estimating the trajectories of multi-objects poses a significant challenge due to data association ambiguity, which leads to a substantial increase in computational requirements. To address such problems, a divide-and-conquer manner has been employed with parallel computation. In this strategy, distinguished objects that have unique labels are grouped based on their statistical dependencies, the intersection of predicted measurements. Several geometry approaches have been used for label grouping since finding all intersected label pairs is clearly infeasible for large-scale tracking problems. This paper proposes an efficient implementation of label grouping for label- partitioned generalized labeled multi-Bernoulli filter framework using a secondary partitioning technique. This allows for parallel computation in the label graph indexing step, avoiding generating and eliminating duplicate comparisons. Additionally, we compare the performance of the proposed technique with several efficient spatial searching algorithms. The results demonstrate the superior performance of the proposed approach on large-scale data sets, enabling scalable trajectory estimation.

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