# Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking

arXiv: https://arxiv.org/abs/2101.08466 · HTML: https://mlsanity.com/paper/2101.08466/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 109 references that are in the map.
- First posted on arXiv 21 Jan 2021.

## Builds on (most cited first, 30 of 49)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [Probabilistic Regression for Visual Tracking](https://mlsanity.com/paper/2003.12565/) (2020)
- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](https://mlsanity.com/paper/2012.12395/) (2020)
- [Ocean: Object-aware Anchor-free Tracking](https://mlsanity.com/paper/2006.10721/) (2020)
- [Learning Multi-Domain Convolutional Neural Networks for Visual Tracking](https://mlsanity.com/paper/1510.07945/) (2015)
- [Siamese Box Adaptive Network for Visual Tracking](https://mlsanity.com/paper/2003.06761/) (2020)
- [Distractor-aware Siamese Networks for Visual Object Tracking](https://mlsanity.com/paper/1808.06048/) (2018)
- [Siam R-CNN: Visual Tracking by Re-Detection](https://mlsanity.com/paper/1911.12836/) (2019)
- [Deeper and Wider Siamese Networks for Real-Time Visual Tracking](https://mlsanity.com/paper/1901.01660/) (2019)
- [SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking](https://mlsanity.com/paper/1911.07241/) (2019)
- [Need for Speed: A Benchmark for Higher Frame Rate Object Tracking](https://mlsanity.com/paper/1703.05884/) (2017)
- [RGB-T Object Tracking:Benchmark and Baseline](https://mlsanity.com/paper/1805.08982/) (2018)
- [Know Your Surroundings: Exploiting Scene Information for Object Tracking](https://mlsanity.com/paper/2003.11014/) (2020)
- [YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video](https://mlsanity.com/paper/1702.00824/) (2017)
- [End-to-end representation learning for Correlation Filter based tracking](https://mlsanity.com/paper/1704.06036/) (2017)
- [High-Performance Long-Term Tracking with Meta-Updater](https://mlsanity.com/paper/2004.00305/) (2020)
- [Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking](https://mlsanity.com/paper/1608.03773/) (2016)
- [Learning Background-Aware Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1703.04590/) (2017)

## Built on by (most cited first, 30 of 31)

- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [Point-to-Box Network for Accurate Object Detection via Single Point Supervision](https://mlsanity.com/paper/2207.06827/) (2022)
- [SynDrone -- Multi-modal UAV Dataset for Urban Scenarios](https://mlsanity.com/paper/2308.10491/) (2023)
- [WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking](https://mlsanity.com/paper/2201.07425/) (2022)
- [Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark](https://mlsanity.com/paper/2103.13933/) (2021)
- [MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats](https://mlsanity.com/paper/2402.03706/) (2024)
- [WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark](https://mlsanity.com/paper/2405.19818/) (2024)
- [Event-based Tiny Object Detection: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2506.23575/) (2025)
- [The 2nd Anti-UAV Workshop & Challenge: Methods and Results](https://mlsanity.com/paper/2108.09909/) (2021)
- [The 3rd Anti-UAV Workshop & Challenge: Methods and Results](https://mlsanity.com/paper/2305.07290/) (2023)
- [Adversarial Attacks on Video Object Segmentation with Hard Region Discovery](https://mlsanity.com/paper/2309.13857/) (2023)
- [Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts](https://mlsanity.com/paper/2305.16450/) (2023)
- [DrIFT: Autonomous Drone Dataset with Integrated Real and Synthetic Data, Flexible Views, and Transformed Domains](https://mlsanity.com/paper/2412.04789/) (2024)
- [LRDDv2: Enhanced Long-Range Drone Detection Dataset with Range Information and Comprehensive Real-World Challenges](https://mlsanity.com/paper/2508.03331/) (2025)
- [UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery](https://mlsanity.com/paper/2104.06219/) (2021)
- [Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction](https://mlsanity.com/paper/2107.00422/) (2021)
- [Multi-step Temporal Modeling for UAV Tracking](https://mlsanity.com/paper/2403.04363/) (2024)
- [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)
- [FlyAwareV2: A Multimodal Cross-Domain UAV Dataset for Urban Scene Understanding](https://mlsanity.com/paper/2510.13243/) (2025)
- [YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs](https://mlsanity.com/paper/2512.18046/) (2025)
- [Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data](https://mlsanity.com/paper/2411.09077/) (2024)
- [AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification](https://mlsanity.com/paper/2412.16928/) (2024)
- [P2Object: Single Point Supervised Object Detection and Instance Segmentation](https://mlsanity.com/paper/2504.07813/) (2025)
- [Learning on the Fly: Replay-Based Continual Object Perception for Indoor Drones](https://mlsanity.com/paper/2602.13440/) (2026)
- [SDD-YOLO: A Small-Target Detection Framework for Ground-to-Air Anti-UAV Surveillance with Edge-Efficient Deployment](https://mlsanity.com/paper/2603.25218/) (2026)
- [Enhanced Small Target Detection via Multi-Modal Fusion and Attention Mechanisms: A YOLOv5 Approach](https://mlsanity.com/paper/2504.11262/) (2025)
- [CETUS: Causal Event-Driven Temporal Modeling With Unified Variable-Rate Scheduling](https://mlsanity.com/paper/2509.13784/) (2025)
- [Perception-to-Pursuit: Track-Centric Temporal Reasoning for Open-World Drone Detection and Autonomous Chasing](https://mlsanity.com/paper/2601.19318/) (2026)
- [UAV-CB: A Complex-Background RGB-T Dataset and Local Frequency Bridge Network for UAV Detection](https://mlsanity.com/paper/2603.17492/) (2026)

## Abstract (arXiv)

Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. With this, monitoring the operation status of UAVs is crucially important. In this work, we consider the task of tracking UAVs, providing rich information such as location and trajectory. To facilitate research on this topic, we propose a dataset, Anti-UAV, with more than 300 video pairs containing over 580k manually annotated bounding boxes. The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs. Furthermore, the advancement of addressing research challenges in Anti-UAV can help the design of anti-UAV systems, leading to better surveillance of UAVs. Besides, a novel approach named dual-flow semantic consistency (DFSC) is proposed for UAV tracking. Modulated by the semantic flow across video sequences, the tracker learns more robust class-level semantic information and obtains more discriminative instance-level features. Experimental results demonstrate that Anti-UAV is very challenging, and the proposed method can effectively improve the tracker's performance. The Anti-UAV benchmark and the code of the proposed approach will be publicly available at https://github.com/ucas-vg/Anti-UAV.