A Bayesian methodology for visual object tracking on stereo sequences

Abstract

A general Bayesian post-processing methodology for performance improvement of object tracking in stereo video sequences is proposed in this paper. We utilize the results of any single channel visual object tracker in a Bayesian framework, in order to refine the tracking accuracy in both stereo video channels. In this framework, a variational Bayesian algorithm is employed, where prior knowledge about the object displacement (movement) is incorporated via a prior distribution. This displacement information is obtained in a preprocessing step, where object displacement is estimated via feature extraction and matching. In parallel, disparity information is extracted and utilized in the same framework. The improvements introduced by the proposed methodology in terms of tracking accuracy are quantified through experimental analysis.