Visual object tracking (VOT) based on discriminative correlation filters (DCF) has received great attention due to its higher computational efficiency and better robustness. However, DCF-based methods suffer from the problem of model contamination. The tracker will drift into the background due to the uncertainties brought by shifting among peaks, which will further lead to the issues of model degradation. To deal with occlusions, a novel Occlusion-Handling Tracker Based on Discriminative Correlation Filters (OHDCF) framework is proposed for online visual object tracking, where an occlusion-ha...
