Event cameras provide high-temporal-resolution visual sensing that is well suited for observing fast-moving aerial objects; however, their use for drone trajectory prediction remains limited. This work introduces an event-only drone forecasting method that exploits propeller-induced motion cues. Propeller rotational speed are extracted directly from raw event data and fused within an RPM-aware Kalman filtering framework. Evaluations on the FRED dataset show that the proposed method outperforms learning-based approaches and vanilla kalman filter in terms of average distance error and final distance error at 0.4s and 0.8s forecasting horizons. The results demonstrate robust and accurate short- and medium-horizon trajectory forecasting without reliance on RGB imagery or training data.
@article{arxiv.2603.01997,
title = {Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering},
author = {Hari Prasanth S. M. and Pejman Habibiroudkenar and Eerik Alamikkotervo and Dimitrios Bouzoulas and Risto Ojala},
journal= {arXiv preprint arXiv:2603.01997},
year = {2026}
}