用事件相机实现高精度异步无人机检测跟踪，解决远距离碎片化问题。
ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras
- 基于运动一致性建模，将碎片化事件块聚类为完整目标。
- 在公开数据集上实现0.813 MOTA，比基线提升12.3%。
- 适合低延迟实时无人机感知场景，尤其适用于远距离观测。
事件相机具备微秒级时间分辨率和高动态范围，适用于低空无人机感知。然而，远距离无人机常产生稀疏、断裂且受噪声污染的事件响应，同一语义目标可能表现为多个空间分离的斑块。直接对斑块进行异步跟踪会导致轨迹重复和身份不稳定。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，直接处理原始事件数据。ASUMOT 将每个无人机建模为一组运动一致的事件斑块：局部运动一致性估计器筛选可靠候选，轻量级多任务验证器提供无人机置信度与运动方向提示，运动一致性聚类将断裂斑块合并为身份一致的无人机轨迹。我们还构建了 ES-UAV，一个高分辨率事件级无人机基准数据集，包含密集语义标注。在公开无人机追踪数据集和 ES-UAV 上的实验表明，ASUMOT 在保持异步事件处理的同时，显著提升了准确率-效率权衡。代码与数据集将公开。
原文摘要 · Abstract (English)
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.
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