Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID
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 .
67e2b68e08c6a250edda264a
Yu-Hsi Chen
claimed_verified
2025-03-25T14:35:46.455Z
67e2063e1ee7f6db889849d6
https://cdn-avatars.huggingface.co/v1/production/uploads/67e2063e1ee7f6db889849d6/ihiwCCqbXlxQ2V_SSGnng.jpeg
Yu-Hsi Chen
user
wish44165
Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID
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 .
A tracking framework using YOLOv12 and BoT-SORT achieves competitive performance in multi-UAV tracking without contrast enhancement or temporal information fusion.
YOLOv12 BoT-SORT multi-UAV tracking thermal infrared video detection tracking Anti-UAV Challenge contrast enhancement temporal information fusion