Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Distributed Multi-Object Tracking under Limited Field of View Sensors

    90853.pdf (2.718Mb)
    Access Status
    Open access
    Authors
    Nguyen, Hoa
    Rezatofighi, H.
    Vo, Ba-Ngu
    Ranasinghe, D.C.
    Date
    2021
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Nguyen, H.V. and Rezatofighi, H. and Vo, B.N. and Ranasinghe, D.C. 2021. Distributed Multi-Object Tracking under Limited Field of View Sensors. IEEE Transactions on Signal Processing. 69: pp. 5329-5344.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2021.3103125
    Additional URLs
    http://dx.doi.org/10.1109/TSP.2021.3103125
    ISSN
    1053-587X
    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/DP160104662
    Remarks

    © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/91029
    Collection
    • Curtin Research Publications
    Abstract

    We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, we develop a novel distributed multi-object tracking algorithm. To accomplish this, we first formalise the concept of label consistency, determine a sufficient condition to achieve it and develop a novel label consensus approach that reduces label inconsistency caused by objects' movements from one node's limited FoV to another. Second, we develop a distributed multi-object fusion algorithm that fuses local multi-object state estimates instead of local multi-object densities. This algorithm: i) requires significantly less processing time than multi-object density fusion methods; ii) achieves better tracking accuracy by considering Optimal Sub-Pattern Assignment (OSPA) tracking errors over several scans rather than a single scan; iii) is agnostic to local multi-object tracking techniques, and only requires each node to provide a set of estimated tracks. Thus, it is not necessary to assume that the nodes maintain multi-object densities, and hence the fusion outcomes do not modify local multi-object densities. Numerical experiments demonstrate our proposed solution's real-time computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios.

    Related items

    Showing items related by title, author, creator and subject.

    • Average Kullback-Leibler divergence for random finite sets
      Battistelli, G.; Chisci, L.; Fantacci, C.; Farina, A.; Vo, Ba-Ngu (2015)
      The paper deals with the fusion of multiobject information over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. To exploit the benefits of sensor ...
    • Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities
      Papi, Francesco; Ba-Ngu, V.; Ba-Tuong, V.; Fantacci, C.; Beard, M. (2015)
      In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the ...
    • An introduction to force and measurement modeling for space object tracking
      Mallick, M.; Rubin, S.; Vo, Ba-Ngu (2013)
      Space object (satellite or space-debris) tracking (SOT) has not received much attention in the Information Fusion community, although the first Fusion conference was held in 1998. A special session on SOT was organized ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.