AI 中文总结
针对远程无人机事件响应问题，提出ASUMOT框架，通过运动一致性建模、局部估计器、多任务验证器和聚类等操作，对无人机进行检测与跟踪，在公共数据和新基准上实验，提升了精度-效率权衡且保持异步处理。
AI 中文摘要
事件相机为低空无人机感知提供微秒级时间分辨率和高动态范围。然而，远程无人机常产生稀疏、碎片化且受噪声污染的事件响应，一个语义目标可能表现为多个空间分离的斑点。直接的斑点级异步跟踪存在重复轨迹和不稳定身份问题。我们提出ASUMOT，一种基于运动一致性的异步无人机检测与跟踪框架，直接对原始事件进行操作。ASUMOT将每个无人机建模为一组运动一致的事件斑点。局部运动一致性估计器触发可靠候选，轻量级多任务验证器提供无人机置信度和运动方向线索，运动一致性聚类将碎片化斑点聚合成身份一致的无人机轨迹。我们还引入ES-UAV，一个具有密集语义标注的高清事件级无人机基准。在公共无人机跟踪数据和ES-UAV上的实验表明，ASUMOT在保持异步事件处理的同时提高了精度-效率权衡。代码和数据集将发布。
英文摘要
Event cameras offer microsecond-level temporal resolution and high dynamic range for low-altitude UAV perception. However, long-range UAVs often produce sparse, fragmented, and noise-contaminated event responses, where one semantic target may appear as multiple spatially separated blobs. Direct blob-level asynchronous tracking therefore suffers from duplicate trajectories and unstable identities. We propose ASUMOT, a motion-consistency-based asynchronous UAV detection and tracking framework operating directly on raw events. ASUMOT models each UAV as a set of motion-consistent event blobs. A local motion-consistency estimator triggers reliable candidates, a lightweight multi-task verifier provides UAV confidence and motion-direction cues, and motion-consistency clustering aggregates fragmented blobs into identity-consistent UAV tracks. We also introduce ES-UAV, a high-definition event-level UAV benchmark with dense semantic annotations. Experiments on public UAV tracking data and ES-UAV show that ASUMOT improves the accuracy--efficiency trade-off while preserving asynchronous event processing. Code and Dataset will be released.
