Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID

Yu-Hsi Chen

Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID: 4 upvotes on Hugging Face Daily Papers, #19 of 34 papers on 2025-03-26. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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