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

arXiv: https://arxiv.org/abs/2506.04048 · HTML: https://mlsanity.com/paper/2506.04048/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 22 of 22)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling](https://mlsanity.com/paper/1904.03375/) (2019)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [EventNet: Asynchronous Recursive Event Processing](https://mlsanity.com/paper/1812.07045/) (2018)
- [Unifying Motion Deblurring and Frame Interpolation with Events](https://mlsanity.com/paper/2203.12178/) (2022)
- [Spiking PointNet: Spiking Neural Networks for Point Clouds](https://mlsanity.com/paper/2310.06232/) (2023)
- [SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition](https://mlsanity.com/paper/2310.07189/) (2023)
- [Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors](https://mlsanity.com/paper/2007.07396/) (2020)
- [Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba](https://mlsanity.com/paper/2405.06116/) (2024)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [Neuromorphic Drone Detection: an Event-RGB Multimodal Approach](https://mlsanity.com/paper/2409.16099/) (2024)
- [Spatiotemporal Filtering for Event-Based Action Recognition](https://mlsanity.com/paper/1903.07067/) (2019)
- [Low-power, Continuous Remote Behavioral Localization with Event Cameras](https://mlsanity.com/paper/2312.03799/) (2023)
- [Neuromorphic Face Analysis: a Survey](https://mlsanity.com/paper/2402.11631/) (2024)
- [A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)](https://mlsanity.com/paper/2404.01568/) (2024)
- [MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice](https://mlsanity.com/paper/2409.03358/) (2024)
- [Learning Normal Flow Directly From Event Neighborhoods](https://mlsanity.com/paper/2412.11284/) (2024)
- [Event Masked Autoencoder: Point-wise Action Recognition with Event-Based Cameras](https://mlsanity.com/paper/2501.01040/) (2025)
- [Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device Triggers for Insect Camera Traps](https://mlsanity.com/paper/2411.14467/) (2024)

## Built on by (most cited first, 9 of 9)

- [FRED: The Florence RGB-Event Drone Dataset](https://mlsanity.com/paper/2506.05163/) (2025)
- [Drone Detection with Event Cameras](https://mlsanity.com/paper/2508.04564/) (2025)
- [M2E-UAV: A Benchmark and Analysis for Onboard Motion-on-Motion Event-Based Tiny UAV Detection](https://mlsanity.com/paper/2605.10496/) (2026)
- [LRDDv3: High-Resolution Long-Range Drone Detection Dataset with Range Information and Thermal Data](https://mlsanity.com/paper/2605.25942/) (2026)
- [ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras](https://mlsanity.com/paper/2607.11303/) (2026)
- [EventRadar: Long-Range Visual UAV Discovery through Spatiotemporal Event Sensing](https://mlsanity.com/paper/2606.11285/) (2026)
- [AE-UAV: An Air-to-Air Event-Based UAV Tracking Benchmark and a Real-Time Frequency-Domain Tracker](https://mlsanity.com/paper/2607.14726/) (2026)
- [SkyEV: RGB-Event UAV detection and tracking dataset and baseline](https://mlsanity.com/paper/2607.18747/) (2026)
- [Bend the Clock: Predicting Ahead to Beat Latency in Event-Based Object Detection](https://mlsanity.com/paper/2609.26919/) (2026)

## Abstract (arXiv)

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.