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Bayesian Loop for Synergistic Change Detection and Tracking

  • Samuele Salti
  • Alessandro Lanza
  • Luigi Di Stefano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

In this paper we investigate Bayesian visual tracking based on change detection. Although in many proposals change detection is key for tracking, little attention has been paid to sound modeling of the interaction between the change detector and the tracker. In this work, we develop a principled framework whereby both processes can virtuously influence each other according to a Bayesian loop: change detection provides a completely specified observation likelihood to the tracker and the tracker provides an informative prior to the change detector.

Keywords

Kalman Filter Change Detection Visual Tracking Soccer Video Observation Likelihood 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Samuele Salti
    • 1
  • Alessandro Lanza
    • 1
  • Luigi Di Stefano
    • 1
  1. 1.Computer Vision LabARCES-DEIS, University of BolognaBolognaItaly

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