Title
Learning residue-aware correlation filters and refining scale for real-time UAV tracking
Abstract
•We propose a novel regularization to model the residue between two neighboring frames, resulting in what we call residue-aware correlation filters, which show better convergence properties in filter learning. Meanwhile, we add spatial and temporal regularizations to boot performance with little additional computational cost.•We propose a novel scale estimation approach for DCF-based trackers by using the GrabCut algorithm to refine the discriminative scale estimates, which can be incorporated easily into any tracking method with discriminative scale estimation to improve precision and accuracy.•We demonstrate the proposed methods on four UAV benchmarks, namely, UAV123@10fps, DTB70, UAVDT and Vistrone2018 (VisDrone2018-test-dev). Experimental results show that the proposed approaches achieves state-of-the-art performance.
Year
DOI
Venue
2022
10.1016/j.patcog.2022.108614
Pattern Recognition
Keywords
DocType
Volume
Residue-aware correlation filters,Discriminative scale estimation,GrabCut,Unmanned aerial vehicle (UAV) tracking
Journal
127
ISSN
Citations 
PageRank 
0031-3203
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Shuiwang Li102.37
Yuting Liu282.15
Qijun Zhao341938.37
Ziliang Feng400.68