FRED: The Florence RGB-Event Drone Dataset
This provides a new benchmark for drone perception research, addressing a domain-specific need for multimodal datasets with fine temporal resolution, though it is incremental as it builds on existing event camera datasets.
The paper tackles the challenge of detecting, tracking, and forecasting fast-moving drones under difficult conditions by introducing the FRED dataset, which includes over 7 hours of annotated RGB and event camera data from 5 drone models in scenarios like rain and adverse lighting.
Small, fast, and lightweight drones present significant challenges for traditional RGB cameras due to their limitations in capturing fast-moving objects, especially under challenging lighting conditions. Event cameras offer an ideal solution, providing high temporal definition and dynamic range, yet existing benchmarks often lack fine temporal resolution or drone-specific motion patterns, hindering progress in these areas. This paper introduces the Florence RGB-Event Drone dataset (FRED), a novel multimodal dataset specifically designed for drone detection, tracking, and trajectory forecasting, combining RGB video and event streams. FRED features more than 7 hours of densely annotated drone trajectories, using 5 different drone models and including challenging scenarios such as rain and adverse lighting conditions. We provide detailed evaluation protocols and standard metrics for each task, facilitating reproducible benchmarking. The authors hope FRED will advance research in high-speed drone perception and multimodal spatiotemporal understanding.