EV-Flying：一个用于野外飞行物体识别的事件基数据集
EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects
AI总结：
本文提出事件基飞行物体数据集EV-Flying，并采用受PointNet启发的轻量级点云方法对鸟类、昆虫和无人机进行分类，以解决传统RGB方法在高速小目标识别中的难题。
摘要：
监测空中物体对于安全、野生动物保护和环境研究至关重要。传统的基于RGB的方法在尺度变化、运动模糊和高速物体运动等挑战面前表现不佳，尤其是对于昆虫和无人机等小型飞行实体。在这项工作中，我们探索了事件基视觉在检测和识别飞行物体方面的潜力，特别是那些可能不遵循短期和长期可预测模式的动物。事件相机提供高时间分辨率、低延迟和对运动模糊的鲁棒性，使其非常适合这项任务。我们引入了EV-Flying，一个飞行物体的事件基数据集，包含人工标注的鸟类、昆虫和无人机，带有时空边界框和轨迹身份标识。为了有效处理异步事件流，我们采用了一种基于点的方法，利用受PointNet启发的轻量级架构。我们的研究考察了使用基于点云的事件表示进行飞行物体分类。所提出的数据集和方法为在现实场景中实现更高效、更可靠的空中物体识别铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
