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

• URL: http://arxiv.org/abs/2503.17237v2
• Date: Mon, 07 Apr 2025 13:03:35 GMT
• Title: Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID
• Authors: Yu-Hsi Chen,
• Abstract summary: Multi-UAV tracking in thermal infrared video is challenging due to low contrast, environmental noise, and small target sizes.<n>We present a tracking framework built on YOLOv12 and BoT-SORT, enhanced with tailored training and inference strategies.<n>We provide implementation details, in-depth experimental analysis, and a discussion of potential improvements.
• Score: 0.03464344220266879
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 at https://github.com/wish44165/YOLOv12-BoT-SORT-ReID .

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