强基线：基于YOLOv12与BoT-SORT-ReID的多无人机目标跟踪

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

March 21, 2025

作者: Yu-Hsi Chen

cs.AI

摘要

在热红外视频中检测和追踪多架无人机（UAV）本质上具有挑战性，主要由于低对比度、环境噪声以及目标尺寸较小。本文提出了一种直接的方法来解决热红外视频中的多无人机追踪问题，利用了检测与追踪领域的最新进展。不同于依赖YOLOv5与DeepSORT的组合，我们构建了一个基于YOLOv12和BoT-SORT的追踪框架，并通过定制化的训练与推理策略进行了增强。我们依据第四届反无人机挑战赛的指标评估了该方法，并展示了其具有竞争力的性能。值得注意的是，我们未采用对比度增强或时域信息融合来丰富无人机特征，却取得了优异成果，这凸显了我们的方法作为多无人机追踪任务的“强基线”地位。文中提供了实现细节、深入的实验分析以及对潜在改进的讨论。代码已公开于https://github.com/wish44165/YOLOv12-BoT-SORT-ReID。

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 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 .