  Recently, correlation filter-based trackers have received extensive attention
due to their simplicity and superior speed. However, such trackers perform
poorly when the target undergoes occlusion, viewpoint change or other
challenging attributes due to pre-defined sampling strategy. To tackle these
issues, in this paper, we propose an adaptive region proposal scheme to
facilitate visual tracking. To be more specific, a novel tracking monitoring
indicator is advocated to forecast tracking failure. Afterwards, we incorporate
detection and scale proposals respectively, to recover from model drift as well
as handle aspect ratio variation. We test the proposed algorithm on several
challenging sequences, which have demonstrated that the proposed tracker
performs favourably against state-of-the-art trackers.
