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• W4311756714 endingPage "110197" @default.
• W4311756714 startingPage "110197" @default.
• W4311756714 abstract "Visual object tracking has achieved remarkable progress in recent years. However, current research in the vision community mainly focuses on tracking of generic objects, while less attention is paid to tracking unmanned aerial vehicle (UAV) especially from a moving platform. In this work, we explore this problem by constructing a UAV to UAV (UAV2UAV) tracking dataset. Specifically, this dataset consists of 44 videos (23k frames). Each video is manually annotated with bounding boxes. To the best of our knowledge, this dataset is the first work dedicated to UAV2UAV tracking. We extensively evaluate 19 state-of-the-art trackers using both hand-crafted feature based and deep-learning based approaches to understand how existing tracking methods perform and to provide a baseline for future works. The evaluation results show that more research works are needed to improve UAV2UAV tracking. In addition, we introduce a novel tracker, which leverages data augmentation technique for UAV tracking to encourage future research. By releasing this dataset, we expect to facilitate future studies and applications of UAV2UAV tracking in both academia and industry communities. Our dataset is available at: https://github.com/hapless19/UAV2UAV-dataset." @default.
• W4311756714 created "2022-12-28" @default.
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• W4311756714 date "2023-02-01" @default.
• W4311756714 modified "2023-10-11" @default.
• W4311756714 title "A UAV to UAV tracking benchmark" @default.
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• W4311756714 doi "https://doi.org/10.1016/j.knosys.2022.110197" @default.
• W4311756714 hasPublicationYear "2023" @default.
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