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The 3rd Anti-UAV Workshop & Challenge: Methods and Results

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arxiv 2305.07290 v2 pith:EMMN6QVA submitted 2023-05-12 cs.CV

Jian Zhao , Jianan Li , Lei Jin , Jiaming Chu , Zhihao Zhang , Jun Wang , Jiangqiang Xia , Kai Wang
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Yang Liu Sadaf Gulshad Jiaojiao Zhao Tianyang Xu Xuefeng Zhu Shihan Liu Zheng Zhu Guibo Zhu Zechao Li Zheng Wang Baigui Sun Yandong Guo Shin ichi Satoh Junliang Xing Jane Shen Shengmei
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classification cs.CV
keywords anti-uavchallengedatasetfirstmethodstrackingworkshopbrief
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The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released. There are two main differences between this year's competition and the previous two. First, we have expanded the existing dataset, and for the first time, released a training set so that participants can focus on improving their models. Second, we set up two tracks for the first time, i.e., Anti-UAV Tracking and Anti-UAV Detection & Tracking. Around 76 participating teams from the globe competed in the 3rd Anti-UAV Challenge. In this paper, we provide a brief summary of the 3rd Anti-UAV Workshop & Challenge including brief introductions to the top three methods in each track. The submission leaderboard will be reopened for researchers that are interested in the Anti-UAV challenge. The benchmark dataset and other information can be found at: https://anti-uav.github.io/.

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Cited by 4 Pith papers

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    TAME applies parallel Mamba state-space models to audio spectrograms and reports state-of-the-art drone trajectory estimation and classification on MMAUD, with unresolved evaluation concerns.

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  4. Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds

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