Semi-supervised ensemble tracking

In this paper, we propose a semi-supervised ensemble tracking approach under the framework of particle filter. The particle filter is used not only for object searching, but also for unlabelled sample generation. By adopting the semi-supervised learning technology, these unlabelled samples which are generated online are utilized to progressively modify the classifier and make the ensemble tracker to be more robust to environment changing. On the other hand, utilizing semi-supervised learning technology can avoid the drifting phenomenons which are often encountered when using supervised learning. Finally, the performance of the proposed approach is evaluated using real visual tracking examples.