Semi-supervised particle filter for visual tracking

In this paper, a semi-supervised particle filter approach is proposed for visual tracking. The combination of semi-supervised learning and particle filter is very natural since the unlabelled samples are generated by particle propagation. In addition, the proposed semi-supervised particle filter can online select different features for robust tracking. To the best knowledge of the authors, this is the first time for the semi-supervised learning technology to be incorporated into the framework of particle filter. Finally, the performance of the proposed approach is evaluated using real visual tracking examples.