发表机构
Rocket Force University of Engineering; Shanghai Jiao Tong University; State Key Laboratory of Submarine Geoscience（火箭军工程大学; 上海交通大学; 海底地球科学国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对空对空无人机跟踪面临的挑战，引入AE-UAV数据集，提出FSFT方法，该方法轻量级且无需训练，能在仅CPU硬件上高速运行，保持高精度，为机载遥感空中目标提供高效强大的解决方案。
AI 中文摘要
空对空（A2A）无人机跟踪是低空航空目标机载遥感的基础。然而，在无人机上部署连续实时跟踪系统面临重大挑战。在A2A场景中，传统基于帧的相机由于动态范围有限和固定时间采样，在低光照、过曝光和高速运动下性能严重下降。虽然事件相机提供了有前景的替代方案，但当前研究受两个主要问题限制：缺乏专用A2A事件数据集和现有跟踪器严重依赖GPU加速及大量训练数据，对资源受限无人机不实用。为填补这些差距，我们引入AE-UAV，一个基于空对空事件的无人机跟踪基准。这是首个用于A2A跟踪的机载捕获事件相机数据集，包含178个带连续时间三次B样条注释的飞行序列。此外，我们提出快速-慢速频域跟踪（FSFT）方法。这个轻量级、无需训练的框架将频域模板匹配与搜索区域预测和基于检测的漂移校正无缝集成。大量实验表明，FSFT在仅CPU硬件上以每秒420帧的超高速运行。它保持了依赖GPU的最先进方法93.97%的精度，同时实现了5.32倍的有效加速，并展现出卓越的时间分辨率泛化能力，为机载遥感空中目标提供了高效且强大的解决方案。数据集和源代码可通过此https URL获取。
英文摘要
Air-to-air (A2A) unmanned aerial vehicle (UAV) tracking is fundamental to airborne remote sensing of low-altitude aerial targets. However, the deployment of continuous, real-time tracking systems on UAVs presents significant challenges. In A2A scenarios, traditional frame-based cameras suffer from severe performance degradation under low illumination, overexposure, and high-speed motion owing to their limited dynamic range and fixed temporal sampling. Although event cameras offer a promising alternative with microsecond temporal resolution and a high dynamic range, current research is bottlenecked by two primary issues: 1) the absence of dedicated A2A event-based datasets, and 2) the heavy reliance of existing trackers on GPU acceleration and extensive training data, rendering them impractical for resource-constrained UAVs. To bridge these gaps, we introduce AE-UAV, an air-to-air event-based UAV tracking benchmark. To the best of our knowledge, this is the first airborne-captured event camera dataset for A2A tracking, comprising 178 flight sequences with continuous-time cubic B-spline annotations. Furthermore, we propose the Fast-Slow Frequency-domain Tracking (FSFT) method. This lightweight, training-free framework seamlessly integrates frequency-domain template matching with search region prediction and detection-based drift correction. Extensive experiments demonstrate that FSFT operates at an ultra-fast 420 frames per second (FPS) on CPU-only hardware. It retains 93.97% of the accuracy of state-of-the-art GPU-dependent methods while delivering a 5.32-fold effective speedup and exhibiting superior temporal resolution generalization, thereby providing a highly efficient and robust solution for airborne remote sensing of aerial targets. The dataset and source code are available at https://github.com/MSP-xEN/AE-UAV.
Comments12 pages, 7 figures. Submitted to IEEE Transactions on Geoscience and Remote Sensing
