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