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Recently, correlation filter-based trackers have received extensive attentiondue to their simplicity and superior speed. However, such trackers performpoorly when the target undergoes occlusion, viewpoint change or otherchallenging attributes due to pre-defined sampling strategy. To tackle theseissues, in this paper, we propose an adaptive region proposal scheme tofacilitate visual tracking. To be more specific, a novel tracking monitoringindicator is advocated to forecast tracking failure. Afterwards, we incorporatedetection and scale proposals respectively, to recover from model drift as wellas handle aspect ratio variation. We test the proposed algorithm on severalchallenging sequences, which have demonstrated that the proposed trackerperforms favourably against state-of-the-art trackers.

Correlation Tracking via Robust Region Proposals