用事件相机数据集提升飞行动物与无人机识别精度
EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects
- 基于事件相机采集飞行物体，用点云方式处理异步数据流
- 构建包含鸟、昆虫、无人机的带时空标注数据集，支持追踪识别
- 适合做低延迟、抗运动模糊的空中目标识别研究者参考
监控空域物体对安全、野生动物保护和环境研究至关重要。传统RGB方法在尺度变化、运动模糊和高速移动场景下表现不佳，尤其对小型飞行动物如昆虫和无人机识别困难。本文探索事件视觉在检测与识别飞行动物中的潜力，尤其是不遵循短期或长期可预测轨迹的生物。事件相机具备高时间分辨率、低延迟和强抗运动模糊能力，非常适合此类任务。我们提出了EV-Flying数据集，包含经人工标注的鸟类、昆虫和无人机，提供时空边界框与跟踪身份信息。为有效处理异步事件流，采用受PointNet启发的轻量级点基方法。研究基于点云表示实现飞行动物分类，所提数据集与方法为真实场景下的高效可靠空中目标识别奠定基础。
原文摘要 · Abstract (English)
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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