Robust long-term tracking of drone is a critical requirement for modern surveillance systems, given their increasing threat potential. While detector-based approaches typically achieve strong frame-level accuracy, they often suffer from temporal inconsistencies caused by frequent detection dropouts. Despite its practical relevance, research on RGB-based drone tracking is still limited and largely reliant on conventional motion models. Meanwhile, foundation models like SAMURAI have established their effectiveness across other domains, exhibiting strong category-agnostic tracking performance. However, their applicability in drone-specific scenarios has not been investigated yet. Motivated by this gap, we present the first systematic evaluation of SAMURAI's potential for robust drone tracking in urban surveillance settings. Furthermore, we introduce a detector-augmented extension of SAMURAI to mitigate sensitivity to bounding-box initialization and sequence length. Our findings demonstrate that the proposed extension significantly improves robustness in complex urban environments, with pronounced benefits in long-duration sequences - especially under drone exit-re-entry events. The incorporation of detector cues yields consistent gains over SAMURAI's zero-shot performance across datasets and metrics, with success rate improvements of up to +0.393 and FNR reductions of up to -0.475.

翻译：鉴于无人机日益增长的威胁潜力，实现无人机的鲁棒长时跟踪已成为现代监控系统的关键需求。基于检测器的方法通常能实现较强的帧级精度，但常因频繁的检测丢失而导致时间不一致性。尽管基于RGB的无人机跟踪具有实际应用价值，相关研究仍较为有限，且主要依赖传统运动模型。与此同时，SAMURAI等基础模型已在其他领域展现出卓越性能，表现出强大的类别无关跟踪能力。然而，其在无人机特定场景中的适用性尚未得到系统研究。基于这一研究空白，我们首次系统评估了SAMURAI在城市监控环境中实现鲁棒无人机跟踪的潜力。进一步地，我们提出了检测器增强的SAMURAI扩展方案，以缓解其对边界框初始化和序列长度的敏感性。实验结果表明，所提扩展方案在复杂城市环境中显著提升了鲁棒性，尤其在长时序列中效果显著——特别是在无人机离场-重入事件场景下。检测器线索的融入使SAMURAI的零样本性能在多个数据集和评估指标上获得持续提升，成功率最高提升+0.393，误报率最大降低-0.475。