EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects

• URL: http://arxiv.org/abs/2506.04048v1
• Date: Wed, 04 Jun 2025 15:14:36 GMT
• Title: EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects
• Authors: Gabriele Magrini, Federico Becattini, Giovanni Colombo, Pietro Pala,
• Abstract summary: Event cameras offer high temporal resolution, low latency, and robustness to motion blur.<n>We introduce EV-Flying, an event-based dataset of flying objects.<n>Our study investigates the classification of flying objects using point cloud-based representations event.
• Score: 8.487185704099925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monitoring aerial objects is crucial for security, wildlife conservation, and environmental studies. Traditional RGB-based approaches struggle with challenges such as scale variations, motion blur, and high-speed object movements, especially for small flying entities like insects and drones. In this work, we explore the potential of event-based vision for detecting and recognizing flying objects, in particular animals that may not follow short and long-term predictable patters. Event cameras offer high temporal resolution, low latency, and robustness to motion blur, making them well-suited for this task. We introduce EV-Flying, an event-based dataset of flying objects, comprising manually annotated birds, insects and drones with spatio-temporal bounding boxes and track identities. To effectively process the asynchronous event streams, we employ a point-based approach leveraging lightweight architectures inspired by PointNet. Our study investigates the classification of flying objects using point cloud-based event representations. The proposed dataset and methodology pave the way for more efficient and reliable aerial object recognition in real-world scenarios.

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