Abstract
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.
Abstract (translated)
在现代监控系统中，无人机的长期稳健追踪是一项关键要求，鉴于其潜在威胁日益增加。虽然基于检测器的方法通常能够在帧级精度上表现出色，但它们往往因频繁出现的目标丢失而产生时间不一致性的问题。尽管这一研究具有实际意义，但在基于RGB图像的无人机跟踪领域，相关研究仍然有限，并且主要依赖传统的运动模型。与此同时，诸如SAMURAI这样的基础模型在其他领域的应用中已经证明了其有效性，表现出强大的类别无关追踪性能。然而，这些模型在特定于无人机场景的应用上尚未进行过探索。 鉴于这一空白，我们首次系统性地评估了SAMURAI在城市监控环境中实现稳健无人机跟踪的潜力。此外，为了缓解对边界框初始化和序列长度敏感的问题，我们还引入了SAMURAI的一个增强版本，并结合检测器的功能。我们的研究发现表明，所提出的扩展显著提高了复杂城市环境下的鲁棒性，尤其是在长时序追踪中表现尤为突出——特别是在无人机离开再返回的事件中。通过融合检测器提供的线索，在所有数据集和评估指标上，与SAMURAI的零样本性能相比，成功率提升了高达+0.393，假阴率减少了最多-0.475。
URL
https://arxiv.org/abs/2601.04798
