用YOLOv12+BoT-SORT实现热成像多无人机跟踪，不依赖增强技术仍达强基线性能
Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID
- 基于YOLOv12检测与BoT-SORT跟踪框架，优化训练与推理策略
- 在第4届反无人机挑战赛评测中表现优异，未使用对比度增强或时序融合
- 适合需要高效、轻量级多无人机跟踪方案的研究者与工程应用
热成像视频中多无人机检测与跟踪因对比度低、环境噪声大、目标尺寸小而极具挑战。本文提出一种直接有效的解决方案，利用最新的检测与跟踪进展，构建基于YOLOv12与BoT-SORT的跟踪框架，并采用定制化训练与推理策略。评估遵循第四届反无人机挑战赛标准，取得具有竞争力的性能。值得注意的是，本方法未使用对比度增强或时序信息融合来丰富特征，仍能达成出色结果，凸显其作为多无人机跟踪任务“强基线”的价值。我们提供了完整实现细节、深入实验分析及潜在改进讨论。代码已公开于 https://github.com/wish44165/YOLOv12-BoT-SORT-ReID。
原文摘要 · Abstract (English)
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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