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

    A labeled random finite set online multi-object tracker for video data

    Access Status
    Fulltext not available
    Authors
    Kim, Du Yong
    Vo, Ba-Ngu
    Vo, Ba Tuong
    Jeon, M.
    Date
    2019
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Kim, D.Y. and Vo, B. and Vo, B.T. and Jeon, M. 2019. A labeled random finite set online multi-object tracker for video data. Pattern Recognition. 90: pp. 377-389.
    Source Title
    Pattern Recognition
    DOI
    10.1016/j.patcog.2019.02.004
    ISSN
    0031-3203
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160104662
    URI
    http://hdl.handle.net/20.500.11937/74333
    Collection
    • Curtin Research Publications
    Abstract

    This paper proposes an online multi-object tracking algorithm for image observations using a top-down Bayesian formulation that seamlessly integrates state estimation, track management, handling of false positives, false negatives and occlusion into a single recursion. This is achieved by modeling the multi-object state as labeled random finite set and using the Bayes recursion to propagate the multi-object filtering density forward in time. The proposed filter updates tracks with detections but switches to image data when detection loss occurs, thereby exploiting the efficiency of detection data and the accuracy of image data. Furthermore the labeled random finite set framework enables the incorporation of prior knowledge that detection loss in the middle of the scene are likely to be due to occlusions. Such prior knowledge can be exploited to improve occlusion handling, especially long occlusions that can lead to premature track termination in on-line multi-object tracking. Tracking performance is compared to state-of-the-art algorithms on synthetic data and well-known benchmark video datasets.

    Related items

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

    • Online multi-object tracking via labeled random finite set with appearance learning
      Kim, Du Yong (2017)
      © 2017 IEEE. In this paper, a novel approach to online multi-object tracking is proposed via Labeled Random Finite Sets (RFS) combined with appearance learning. The Labeled RFS formulation of the multi-object state naturally ...
    • A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling
      Ong, Jonah ; Vo, Ba Tuong ; Vo, Ba-Ngu ; Kim, Du Yong ; Nordholm, Sven (2022)
      This paper proposes an online multi-camera multi-object tracker that only requires monocular detector training, independent of the multi-camera configurations, allowing seamless extension/deletion of cameras without ...
    • 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 ...
    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.