  Unmanned aerial vehicle (UAV)-based tracking is attracting increasing
attention and developing rapidly in applications such as agriculture, aviation,
navigation, transportation and public security. Recently, discriminative
correlation filters (DCF)-based trackers have stood out in UAV tracking
community for their high efficiency and appealing robustness on a single CPU.
However, due to limited onboard computation resources and other challenges the
efficiency and accuracy of existing DCF-based approaches is still not
satisfying. In this paper, we explore using segmentation by the GrabCut to
improve the wildly adopted discriminative scale estimation in DCF-based
trackers, which, as a mater of fact, greatly impacts the precision and accuracy
of the trackers since accumulated scale error degrades the appearance model as
online updating goes on. Meanwhile, inspired by residue representation, we
exploit the residue nature inherent to videos and propose residue-aware
correlation filters that show better convergence properties in filter learning.
Extensive experiments are conducted on four UAV benchmarks, namely,
UAV123@10fps, DTB70, UAVDT and Vistrone2018 (VisDrone2018-test-dev). The
results show that our method achieves state-of-the-art performance.
