Variational Bayesian inference for forward-backward visual tracking in stereo sequences
Giannis Chantas, Nikos Nikolaidis, Ioannis Pitas · 2012
In this paper we propose a Bayesian framework for accurate object tracking in stereoscopic sequences. Object detection and forward tracking are first combined according to predefined rules to get a first set of tracked regions candidates. Backward tracking is then applied to provide another set of possible object localizations. Moreover, this strategy is applied herein in stereoscopic video. We introduce a Bayesian inference algorithm which is used to merge the information of both forward and backward tracking in order to refine the tracked region localization results. Experiments, performed on face tracking, show that the proposed method provides higher tracking accuracy than a forward tracker.