  In this paper, we propose a hierarchical feature-aware tracking framework for
efficient visual tracking. Recent years, ensembled trackers which combine
multiple component trackers have achieved impressive performance. In ensembled
trackers, the decision of results is usually a post-event process, i.e.,
tracking result for each tracker is first obtained and then the suitable one is
selected according to result ensemble. In this paper, we propose a pre-event
method. We construct an expert pool with each expert being one set of features.
For each frame, several experts are first selected in the pool according to
their past performance and then they are used to predict the object. The
selection rate of each expert in the pool is then updated and tracking result
is obtained according to result ensemble. We propose a novel pre-known
expert-adaptive selection strategy. Since the process is more efficient, more
experts can be constructed by fusing more types of features which leads to more
robustness. Moreover, with the novel expert selection strategy, overfitting
caused by fixed experts for each frame can be mitigated. Experiments on several
public available datasets demonstrate the superiority of the proposed method
and its state-of-the-art performance among ensembled trackers.
