Bayesian loop for synergistic change detection and tracking

Authors:
Samuele Salti;Alessandro Lanza;Luigi Di Stefano
Affiliations:
Computer Vision Lab, ARCES-DEIS, University of Bologna, Bologna, Italy;Computer Vision Lab, ARCES-DEIS, University of Bologna, Bologna, Italy;Computer Vision Lab, ARCES-DEIS, University of Bologna, Bologna, Italy
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Year:
2010

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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.