DPTrack：方向性核引导的提示学习用于鲁棒的夜间航空跟踪
DPTrack:Directional Kernel-Guided Prompt Learning for Robust Nighttime Aerial Tracking
AI总结：
针对夜间航空跟踪中提示模糊问题，提出DPTrack，通过方向性核编码目标属性特征生成精确提示，结合拓扑结构与空间门控，在基准上表现优越。
摘要：
现有的基于提示学习的夜间航空跟踪器仅依赖空间定位监督，这无法提供指向目标特征的细粒度线索，不可避免地产生模糊的提示。这一局限性削弱了跟踪器准确聚焦于目标特征的能力，导致跟踪器性能仍然不佳。为解决此问题，我们提出了DPTrack，一种专为夜间场景设计的基于提示的航空跟踪器，通过将给定对象的属性特征编码到富含细粒度线索的方向性核中，以生成精确的提示。具体而言，受视觉仿生学启发，DPTrack首先分层捕获对象的拓扑结构，利用拓扑属性丰富特征表示。随后，一个编码器将这些拓扑感知特征压缩为方向性核，该核作为核心引导信号，明确封装了对象的细粒度属性线索。最后，一个基于通道-类别对应属性的核引导提示模块将核传播到搜索区域的特征上，以精确定位目标特征的位置并将其转换为精确的提示，并结合空间门控以实现鲁棒的夜间跟踪。在既有基准上的广泛评估表明DPTrack具有优越性能。我们的代码将在https://github.com/zzq-vipsl/DPTrack提供。
英文摘要：
Existing nighttime aerial trackers based on prompt learning rely solely on spatial localization supervision, which fails to provide fine-grained cues that point to target features and inevitably produces vague prompts. This limitation impairs the tracker's ability to accurately focus on the object features and results in trackers still performing poorly. To address this issue, we propose DPTrack, a prompt-based aerial tracker designed for nighttime scenarios by encoding the given object's attribute features into the directional kernel enriched with fine-grained cues to generate precise prompts. Specifically, drawing inspiration from visual bionics, DPTrack first hierarchically captures the object's topological structure, leveraging topological attributes to enrich the feature representation. Subsequently, an encoder condenses these topology-aware features into the directional kernel, which serves as the core guidance signal that explicitly encapsulates the object's fine-grained attribute cues. Finally, a kernel-guided prompt module built on channel-category correspondence attributes propagates the kernel across the features of the search region to pinpoint the positions of target features and convert them into precise prompts, integrating spatial gating for robust nighttime tracking. Extensive evaluations on established benchmarks demonstrate DPTrack's superior performance. Our code will be available at https://github.com/zzq-vipsl/DPTrack.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
