FRED: The Florence RGB-Event Drone Dataset

• URL: http://arxiv.org/abs/2506.05163v1
• Date: Thu, 05 Jun 2025 15:40:41 GMT
• Title: FRED: The Florence RGB-Event Drone Dataset
• Authors: Gabriele Magrini, Niccolò Marini, Federico Becattini, Lorenzo Berlincioni, Niccolò Biondi, Pietro Pala, Alberto Del Bimbo,
• Abstract summary: 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.<n>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.<n>This paper introduces the Florence RGB-Event Drone dataset (REDF), a novel multimodal dataset specifically designed for drone detection, tracking, and trajectory forecasting, combining RGB video and streams.
• Score: 23.020669715621604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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