  Correlation filter-based trackers heavily suffer from the problem of multiple
peaks in their response maps incurred by occlusions. Moreover, the whole
tracking pipeline may break down due to the uncertainties brought by shifting
among peaks, which will further lead to the degraded correlation filter model.
To alleviate the drift problem caused by occlusions, we propose a novel scheme
to choose the specific filter model according to different scenarios.
Specifically, an effective measurement function is designed to evaluate the
quality of filter response. A sophisticated strategy is employed to judge
whether occlusions occur, and then decide how to update the filter models. In
addition, we take advantage of both log-polar method and pyramid-like approach
to estimate the best scale of the target. We evaluate our proposed approach on
VOT2018 challenge and OTB100 dataset, whose experimental result shows that the
proposed tracker achieves the promising performance compared against the
state-of-the-art trackers.
