# GL-DT: Multi-UAV Detection and Tracking with Global-Local Integration

arXiv: https://arxiv.org/abs/2510.09092 · HTML: https://mlsanity.com/paper/2510.09092/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [Real-Time Flying Object Detection with YOLOv8](https://mlsanity.com/paper/2305.09972/) (2023)
- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark](https://mlsanity.com/paper/2103.13933/) (2021)
- [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://mlsanity.com/paper/2504.11967/) (2025)
- [The 3rd Anti-UAV Workshop & Challenge: Methods and Results](https://mlsanity.com/paper/2305.07290/) (2023)
- [Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion](https://mlsanity.com/paper/2312.11008/) (2023)
- [FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking](https://mlsanity.com/paper/2504.13604/) (2025)
- [Domain Adaptive Detection of MAVs: A Benchmark and Noise Suppression Network](https://mlsanity.com/paper/2403.16669/) (2024)
- [Real-Time Detection for Small UAVs: Combining YOLO and Multi-frame Motion Analysis](https://mlsanity.com/paper/2411.02582/) (2024)
- [Motion-guided small MAV detection in complex and non-planar scenes](https://mlsanity.com/paper/2410.10527/) (2024)
- [Drone Detection and Tracking with YOLO and a Rule-based Method](https://mlsanity.com/paper/2502.05292/) (2025)

## Built on by (most cited first, 2 of 2)

- [MF-UAVPose6D: A Model-Free Monocular 6-DoF Pose Estimation Framework for Fixed-Wing UAVs](https://mlsanity.com/paper/2606.29697/) (2026)
- [EECTracker: Swarm Motion Prior-Guided Feature Compensation for Airborne Optical UAV Swarm Tracking](https://mlsanity.com/paper/2609.15171/) (2026)

## Abstract (arXiv)

The extensive application of unmanned aerial vehicles (UAVs) in military reconnaissance, environmental monitoring, and related domains has created an urgent need for accurate and efficient multi-object tracking (MOT) technologies, which are also essential for UAV situational awareness. However, complex backgrounds, small-scale targets, and frequent occlusions and interactions continue to challenge existing methods in terms of detection accuracy and trajectory continuity. To address these issues, this paper proposes the Global-Local Detection and Tracking (GL-DT) framework. It employs a Spatio-Temporal Feature Fusion (STFF) module to jointly model motion and appearance features, combined with a global-local collaborative detection strategy, effectively enhancing small-target detection. Building upon this, the JPTrack tracking algorithm is introduced to mitigate common issues such as ID switches and trajectory fragmentation. Experimental results demonstrate that the proposed approach significantly improves the continuity and stability of MOT while maintaining real-time performance, providing strong support for the advancement of UAV detection and tracking technologies.