Nighttime UAV tracking under low-illuminated scenarios has achieved great progress by domain adaptation (DA). However, previous DA training-based works are deficient in narrowing the discrepancy of temporal contexts for UAV trackers. To address the issue, this work proposes a prompt-driven temporal domain adaptation training framework to fully utilize temporal contexts for challenging nighttime UAV tracking, i.e., TDA. Specifically, the proposed framework aligns the distribution of temporal contexts from daytime and nighttime domains by training the temporal feature generator against the discriminator. The temporal-consistent discriminator progressively extracts shared domain-specific features to generate coherent domain discrimination results in the time series. Additionally, to obtain high-quality training samples, a prompt-driven object miner is employed to precisely locate objects in unannotated nighttime videos. Moreover, a new benchmark for long-term nighttime UAV tracking is constructed. Exhaustive evaluations on both public and self-constructed nighttime benchmarks demonstrate the remarkable performance of the tracker trained in TDA framework, i.e., TDA-Track. Real-world tests at nighttime also show its practicality. The code and demo videos are available at https://github.com/vision4robotics/TDA-Track.

翻译：在低光照场景下的夜间无人机跟踪通过域自适应技术已取得显著进展。然而，先前基于域自适应训练的研究在缩小无人机跟踪器时序上下文差异方面存在不足。为解决这一问题，本文提出一种提示驱动的时序域自适应训练框架，以充分利用时序上下文处理具有挑战性的夜间无人机跟踪任务，即TDA。具体而言，所提框架通过训练时序特征生成器对抗判别器，对齐白天与夜间域的时序上下文分布。时序一致判别器逐步提取共享的域特定特征，在时间序列中生成连贯的域判别结果。此外，为获得高质量训练样本，采用提示驱动的目标挖掘器精确定位未标注夜间视频中的目标。同时，构建了新的长期夜间无人机跟踪基准测试集。在公开及自建夜间基准测试上的详尽评估表明，基于TDA框架训练的跟踪器（即TDA-Track）具有卓越性能。夜间实际场景测试也验证了其实用性。代码与演示视频详见 https://github.com/vision4robotics/TDA-Track。