面向无人机反无人机跟踪的语义感知时序自适应
Semantic-Aware Temporal Adaptation for UAV Anti-UAV Tracking
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中文总结 AI 辅助
针对无人机反无人机跟踪的双动态场景挑战，提出SATATrack框架，结合语义锚点与时序自适应技术，在基准任务上实现最优性能。
中文摘要 AI 辅助
无人机反无人机跟踪是一项新兴的低空安全任务，利用运动观测无人机的机载相机定位对抗性无人机。它不同于常规无人机跟踪和基于地面的反无人机跟踪，因为相机平台与目标会同时运动。这种双动态场景会引发快速视角变化、运动模糊、尺度变化以及视觉上相似的干扰项，使得可靠的外观匹配变得困难。在这种快速变化的条件下，固定的视觉表征往往不足，因为目标外观变得不可靠，特征分布可能偏离训练域。目标的语言描述在帧间保持稳定，因此可作为语义锚点用于时序状态传播，而在线特征分布对齐可减少视频特定的测试时偏移。本文提出了用于无人机反无人机跟踪的语义感知时序自适应框架（Semantic-Aware Temporal Adaptation, SATATrack）。SATATrack引入语义感知上下文传播（Semantic-Aware Context Propagation, SACP），利用目标描述指导骨干网络各阶段的时序上下文传播，在快速外观变化下保留目标身份。训练期间使用辅助对比正则化器，抑制对语义相似背景区域的响应。推理期间，时序感知分布对齐（Temporal-Aware Distribution Alignment, TADA）在不更新模型参数的情况下在线对齐特征分布，结合近期帧估计与训练时统计以保证稳定性。SATATrack在UAV-Anti-UAV基准上实现了最先进的性能，同时在反无人机和无人机目标跟踪任务中保持竞争力，代码将在该网址提供。
英文摘要
UAV Anti-UAV tracking is an emerging low-altitude security task for localizing an adversarial UAV using the onboard camera of a moving observer UAV. It differs from conventional UAV tracking and ground-based Anti-UAV tracking because both the camera platform and the target move simultaneously. This dual-dynamic setting induces rapid viewpoint changes, motion blur, scale variation, and visually similar distractors, making reliable appearance matching difficult. Under such rapidly changing conditions, fixed visual representations are often insufficient because target appearance becomes unreliable and feature distributions may deviate from the training domain. The target language description remains stable across frames and can therefore serve as a semantic anchor for temporal state propagation, while online feature-distribution alignment can reduce video-specific test-time shifts. In this paper, we propose \emph{SATATrack}, a Semantic-Aware Temporal Adaptation framework for UAV Anti-UAV tracking. SATATrack introduces Semantic-Aware Context Propagation (SACP), which uses the target description to guide temporal context propagation across backbone stages and preserve target identity under rapid appearance changes. An auxiliary contrastive regularizer is used during training to discourage responses to semantically similar background regions. During inference, Temporal-Aware Distribution Alignment (TADA) aligns feature distributions online without updating model parameters, combining recent-frame estimates with training-time statistics for stability. SATATrack achieves state-of-the-art performance on the UAV-Anti-UAV benchmark while remaining competitive in Anti-UAV and UAV object tracking tasks. The code will be available at https://github.com/XiaozhenQiao/SATATrack.
