Blob trackers have become increasingly powerful in recent years largely due to the adoption of statistical appearance models which allow effective background subtraction and robust tracking of deforming foreground objects. It has been standard, however, to treat background and foreground modelling as separate processes-background subtraction is followed by blob detection and tracking-which prevents a principled computation of image likelihoods. This paper presents two theoretical advances which address this limitation and lead to a robust multiple-person tracking system suitable for single-camera real-time surveillance applications. The first innovation is a multi-blob likelihood function which assigns directly comparable likelihoods to hypotheses containing different numbers of objects. This likelihood function has a rigorous mathematical basis: it is adapted from the theory of Bayesian correlation, but uses the assumption of a static camera to create a more specific background model while retaining a unified approach to background and foreground modelling. Second we introduce a Bayesian filter for tracking multiple objects when the number of objects present is unknown and varies over time. We show how a particle filter can be used to perform joint inference on both the number of objects present and their configurations. Finally we demonstrate that our system runs comfortably in real time on a modest workstation when the number of blobs in the scene is small.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    BraMBLe: a Bayesian multiple-blob tracker


    Contributors:
    Isard, M. (author) / MacCormick, J. (author)


    Publication date :

    2001-01-01


    Size :

    1102064 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    BraMBLe: A Bayesian Multiple-Blob Tracker

    Isard, M. / MacCormick, J. / IEEE | British Library Conference Proceedings | 2001


    'Bramble Bush Bay'

    Dunston, Richard | Online Contents | 1994


    View matching with blob features

    Forssen, P. E. / Moe, A. | British Library Online Contents | 2009



    Mean-Shift Blob Tracking through Scale Space

    Collins, R. / IEEE | British Library Conference Proceedings | 2003