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

    Visual tracking of multiple targets by Multi-Bernoulli filtering of background subtracted image data

    Access Status
    Fulltext not available
    Authors
    Hoseinnezhad, R.
    Vo, Ba-Ngu
    Vu, T.N.
    Date
    2011
    Type
    Book Chapter
    
    Metadata
    Show full item record
    Citation
    Hoseinnezhad, R. and Vo, B. and Vu, T.N. 2011. Visual tracking of multiple targets by Multi-Bernoulli filtering of background subtracted image data, in Tan, Y. and Shi, Y. and Chai, Y. and Wang, G. (ed), Advances in Swarm Intelligence: Lecture Notes in Computer Science Part 2. 6729: pp. 509-518. Berlin, Heidelberg: Springer.
    Source Title
    Advances in Swarm Intelligence Lecture Notes in Computer Science Volume 6729
    DOI
    10.1007/978-3-642-21524-7_63
    ISBN
    978-3-642-21523-0
    URI
    http://hdl.handle.net/20.500.11937/45604
    Collection
    • Curtin Research Publications
    Abstract

    Most visual multi-target tracking techniques in the literature employ a detection routine to map the image data to point measurements that are usually further processed by a filter. In this paper, we present a visual tracking technique based on a multi-target filtering algorithm that operates directly on the image observations and does not require any detection nor training patterns. Instead, we use the recent history of image data for non-parametric background subtraction and apply an efficient multi-target filtering technique, known as the multi-Bernoulli filter, on the resulting grey scale image data. In our experiments, we applied our method to track multiple people in three video sequences from the CAVIAR dataset. The results show that our method can automatically track multiple interacting targets and quickly finds targets entering or leaving the scene.

    Related items

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

    • Visual Tracking in Background Subtracted Image Sequences via Multi-Bernoulli Filtering
      Hoseinnezhad, R.; Vo, Ba-Ngu; Vo, Ba Tuong (2013)
      This correspondence presents a novel method for simultaneous tracking of multiple non-stationary targets in video. Our method operates directly on the video data and does not require any detection. We propose a multi-target ...
    • Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach
      Kim, Du Yong (2018)
      © 2018 ISIF This paper proposes a robust multi-target tracking algorithm for uncertainty in dynamic motion modeling. To address this issue, the multi-target tracking problem is formulated under random finite set (RFS) ...
    • Multi-Scan Generalized Labeled Multi-Bernoulli Filter
      Vo, Ba Tuong; Vo, Ba-Ngu (2018)
      © 2018 ISIF This paper extends the generalized labeled multi-Bernoulli (GLMB) tracking filter to a batch multi-target tracker. In a labeled random finite set formulation, a multi-target tracking filter propagates 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.