Online failure detection and correction for Bayesian sparse feature-based object tracking

Online evaluation of tracking algorithms is an important task in real time tracking systems to detect failures. In visual object tracking based on sparse features, detecting the failure of one of the feature points (corners) and correcting it will improve the performance of the tracker as a whole. In this paper a time reversed Markov chain is applied as evaluation technique to identify the failed trackers and Partial Least Square regression is used for learning the correlation between feature points from training data set. The detected feature point trackers are recovered from the knowledge of the learned correlation model. The results are explained on a Bayesian algorithm for rigid/nonrigid 2D visual object tracking. The experimental outcomes show a global performance improvement of the tracking algorithm even in the presence of clutter.