ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras
Abstract
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.
Cite
@article{arxiv.2607.11303,
title = {ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras},
author = {Baofeng Jia and Xiaoyu Chen and Jingyuan Zhang and Zongze Wu and Haochen li and Jing Han and Lianfa Bai},
journal= {arXiv preprint arXiv:2607.11303},
year = {2026}
}