Detecting and tracking multiple unmanned aerial vehicles (UAVs) in thermal infrared video is inherently challenging due to low contrast, environmental noise, and small target sizes. This paper provides a straightforward approach to address multi-UAV tracking in thermal infrared video, leveraging recent advances in detection and tracking. Instead of relying on the well-established YOLOv5 with DeepSORT combination, we present a tracking framework built on YOLOv12 and BoT-SORT, enhanced with tailored training and inference strategies. We evaluate our approach following the 4th Anti-UAV Challenge metrics and reach competitive performance. Notably, we achieved strong results without using contrast enhancement or temporal information fusion to enrich UAV features, highlighting our approach as a "Strong Baseline" for multi-UAV tracking tasks. We provide implementation details, in-depth experimental analysis, and a discussion of potential improvements. The code is available atthis https URL.
View on arXiv@article{chen2025_2503.17237,
title={ Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID },
author={ Yu-Hsi Chen },
journal={arXiv preprint arXiv:2503.17237},
year={ 2025 }
}