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The 2nd Anti-UAV Workshop & Challenge: Methods and Results

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arxiv 2108.09909 v3 pith:TEIMUWVB submitted 2021-08-23 cs.CV

Jian Zhao , Gang Wang , Jianan Li , Lei Jin , Nana Fan , Min Wang , Xiaojuan Wang , Ting Yong
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Yafeng Deng Yandong Guo Shiming Ge Guodong Guo
This is my paper · ORCID
classification cs.CV
keywords anti-uavchallengedatasetmethodsworkshopbriefmulti-scalesubset
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abstract

The 2nd 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 subsets in the dataset, $i.e.$, the test-dev subset and test-challenge subset. Both subsets consist of 140 thermal infrared video sequences, spanning multiple occurrences of multi-scale UAVs. Around 24 participating teams from the globe competed in the 2nd Anti-UAV Challenge. In this paper, we provide a brief summary of the 2nd Anti-UAV Workshop \& Challenge including brief introductions to the top three methods.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 3 Pith papers

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    AV-DTEC fuses audio and visual features using a state-space model and an adaptive teacher-student mechanism to estimate drone trajectories and classify drone types, achieving state-of-the-art results on the MMAUD data...

  2. TAME: Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification

    cs.SD 2024-12 conditional novelty 5.0 of 10

    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.

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

    cs.CV 2024-12 conditional novelty 4.0 of 10

    An unsupervised LiDAR clustering and spline method estimates UAV 3D trajectories from sparse point clouds, reporting 1.35 m RMSE on the MMAUD v2/v3 benchmark.

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