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

arXiv: https://arxiv.org/abs/2305.07290 · HTML: https://mlsanity.com/paper/2305.07290/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 10 of 10)

- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [Siam R-CNN: Visual Tracking by Re-Detection](https://mlsanity.com/paper/1911.12836/) (2019)
- [Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation](https://mlsanity.com/paper/2007.02024/) (2020)
- [High-Performance Long-Term Tracking with Meta-Updater](https://mlsanity.com/paper/2004.00305/) (2020)
- [Effective Fusion Factor in FPN for Tiny Object Detection](https://mlsanity.com/paper/2011.02298/) (2020)
- [Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking](https://mlsanity.com/paper/2101.08466/) (2021)
- [How to Train Your Energy-Based Model for Regression](https://mlsanity.com/paper/2005.01698/) (2020)
- [Object Relation Detection Based on One-shot Learning](https://mlsanity.com/paper/1807.05857/) (2018)

## Built on by (most cited first, 9 of 9)

- [MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats](https://mlsanity.com/paper/2402.03706/) (2024)
- [Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System](https://mlsanity.com/paper/2306.15767/) (2023)
- [Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation](https://mlsanity.com/paper/2504.19347/) (2025)
- [A Simple Detector with Frame Dynamics is a Strong Tracker](https://mlsanity.com/paper/2505.04917/) (2025)
- [Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds](https://mlsanity.com/paper/2412.12716/) (2024)
- [TAME: Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification](https://mlsanity.com/paper/2412.13037/) (2024)
- [GL-DT: Multi-UAV Detection and Tracking with Global-Local Integration](https://mlsanity.com/paper/2510.09092/) (2025)
- [UAVDB: Point-Guided Masks for UAV Detection and Segmentation](https://mlsanity.com/paper/2409.06490/) (2024)
- [AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification](https://mlsanity.com/paper/2412.16928/) (2024)

## Abstract (arXiv)

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/.