Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering
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 sho...
| Authors | Hari Prasanth S. M., Pejman Habibiroudkenar, Eerik Alamikkotervo et al. |
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| Primary category | cs.CV |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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