ARTracker: Compute a More Accurate and Robust Correlation Filter for UAV Tracking
Junjie Chen, Tingfa Xu, Bo Huang, Ying Wang, Jianan Li · IEEE Geoscience and Remote Sensing Letters · 2022
Unmanned aerial vehicle (UAV) tracking focus on tracking moving targets from flying platforms, where the target undergoes a lot of aspect ratio changes,i.e., viewpoint change, rotation. Discriminative correlation filter (DCF) based method shows a promising solution to UAV tracking due to its high computational efficiency. DCF based trackers apply a fixed-bandwidth Gaussian function label for model training and incremental update to adapt to the dramatic appearance changes in target during tracking. However, due to the poor regression ability of correlation filter, DCF trackers is unable to describe the target state when the aspect ratio changes, thus hurting the tracking performance. To alleviate this, we propose a novelty correlation filter, which constructs a Gaussian-like function label for correlation filter training. The label fully considers the aspect ratio distribution of the target, which facilitates the training of a more robust tracker. Furthermore, an accurate incremental update is proposed to mitigate model degradation by combining target samples with adaptive aspect ratios. Extensive experiments are conducted on three popular UAV benchmarks,i.e., VisDrone2018-test-dev, UAV20L and DTB70. Results well demonstrate the superiority of the proposed method over both DCF and deep based trackers. Code will be released soon.