BraMBLe: A Bayesian Multiple-Blob Tracker

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...

• Type: Conference
• Year: 2001
• Where: ICCV
• Authors: Michael Isard, John MacCormick
• Keywords: Computer Vision, Effective Background Subtraction, Foreground Modelling, Foreground Objects, ICCV 2001, Robust Multiple-person Tracking, Specific Background Model