Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID
Authors: name: Yu-Hsi Chen

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 YOLOv5 with the
DeepSORT pipeline, we present a tracking framework built on YOLOv12 and
BoT-SORT, enhanced with tailored training and inference strategies. We evaluate
our approach following the metrics from the 4th Anti-UAV Challenge and
demonstrate competitive performance. Notably, we achieve strong results without
using contrast enhancement or temporal information fusion to enrich UAV
features, highlighting our approach as a "Strong Baseline" for the multi-UAV
tracking task. We provide implementation details, in-depth experimental
analysis, and a discussion of potential improvements. The code is available at
https://github.com/wish44165/YOLOv12-BoT-SORT-ReID .