WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking

• URL: http://arxiv.org/abs/2201.07425v1
• Date: Wed, 19 Jan 2022 05:39:42 GMT
• Title: WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking
• Authors: Chunhui Zhang, Guanjie Huang, Li Liu, Shan Huang, Yinan Yang, Yuxuan Zhang, Xiang Wan, Shiming Ge
• Abstract summary: WebUAV-3M is a million-scale Unmanned Aerial Vehicle (UAV) tracking benchmark. We collect 4,485 videos with more than 3M frames from the Internet. WebUAV-3M is by far the largest public UAV tracking benchmark.
• Score: 40.83427270297245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we contribute a new million-scale Unmanned Aerial Vehicle (UAV) tracking benchmark, called WebUAV-3M. Firstly, we collect 4,485 videos with more than 3M frames from the Internet. Then, an efficient and scalable Semi-Automatic Target Annotation (SATA) pipeline is devised to label the tremendous WebUAV-3M in every frame. To the best of our knowledge, the densely bounding box annotated WebUAV-3M is by far the largest public UAV tracking benchmark. We expect to pave the way for the follow-up study in the UAV tracking by establishing a million-scale annotated benchmark covering a wide range of target categories. Moreover, considering the close connections among visual appearance, natural language and audio, we enrich WebUAV-3M by providing natural language specification and audio description, encouraging the exploration of natural language features and audio cues for UAV tracking. Equipped with this benchmark, we delve into million-scale deep UAV tracking problems, aiming to provide the community with a dedicated large-scale benchmark for training deep UAV trackers and evaluating UAV tracking approaches. Extensive experiments on WebUAV-3M demonstrate that there is still a big room for robust deep UAV tracking improvements. The dataset, toolkits and baseline results will be available at \url{https://github.com/983632847/WebUAV-3M}.

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