Performance evaluation of low resolution visual tracking for unmanned aerial vehicles

Yong Wang, Xian Wei*, Hao Shen, Jilin Hu, Lingkun Luo


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3 Citationer (Scopus)


Several datasets for unmanned aerial vehicle (UAV) visual tracking research have been released in recent years. Despite their usefulness, whether they are sufficient for understanding the strengths and weakness of different resolution videos tracking remains questionable. Tracking in low resolution videos is a critical problem in UAV tracking. To address this issue, we construct a group of low resolution tracking datasets and study the performance of different trackers on these datasets. We find that some trackers suffered more performance degradation than others, which brings to light a previously unexplored aspect of the tracking methods. The relative rank of these trackers based on their tracking results on the datasets may change in the presence of low resolution. Based on these findings, we develop a multiple feature tracking framework which takes advantage of image enhancement scheme to improve image quality. In addition, we utilize the forward and backward tracking to evaluate multiple feature tracking results. Experimental results demonstrate that our tracker is competitive in performance to state-of-the-art methods in different resolutions scenarios. We believe our studies can provide a solid baseline when conducting experiments for low resolution UAV tracking research.

TidsskriftNeural Computing and Applications
Udgave nummer7
Sider (fra-til)2229-2248
Antal sider20
StatusUdgivet - apr. 2021

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© 2020, Springer-Verlag London Ltd., part of Springer Nature.


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