# ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras

arXiv: https://arxiv.org/abs/2607.11303 · HTML: https://mlsanity.com/paper/2607.11303/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Builds on (most cited first, 27 of 27)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [Event-based Asynchronous Sparse Convolutional Networks](https://mlsanity.com/paper/2003.09148/) (2020)
- [Object Detection with Spiking Neural Networks on Automotive Event Data](https://mlsanity.com/paper/2205.04339/) (2022)
- [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://mlsanity.com/paper/2410.13842/) (2024)
- [Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline](https://mlsanity.com/paper/2309.14611/) (2023)
- [Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception](https://mlsanity.com/paper/1807.10936/) (2018)
- [Effective Fusion Factor in FPN for Tiny Object Detection](https://mlsanity.com/paper/2011.02298/) (2020)
- [Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking](https://mlsanity.com/paper/2002.05583/) (2020)
- [Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset](https://mlsanity.com/paper/2412.06647/) (2024)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [Asynchronous Blob Tracker for Event Cameras](https://mlsanity.com/paper/2307.10593/) (2023)
- [FRED: The Florence RGB-Event Drone Dataset](https://mlsanity.com/paper/2506.05163/) (2025)
- [Neuromorphic Drone Detection: an Event-RGB Multimodal Approach](https://mlsanity.com/paper/2409.16099/) (2024)
- [Event-based Tiny Object Detection: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2506.23575/) (2025)
- [Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors](https://mlsanity.com/paper/2403.11875/) (2024)
- [EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects](https://mlsanity.com/paper/2506.04048/) (2025)
- [Drone Detection with Event Cameras](https://mlsanity.com/paper/2508.04564/) (2025)
- [Hybrid Spiking Vision Transformer for Object Detection with Event Cameras](https://mlsanity.com/paper/2505.07715/) (2025)
- [Asynchronous Multi-Object Tracking with an Event Camera](https://mlsanity.com/paper/2505.08126/) (2025)
- [EvDetMAV: Generalized MAV Detection from Moving Event Cameras](https://mlsanity.com/paper/2506.19416/) (2025)
- [Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire](https://mlsanity.com/paper/2509.12997/) (2025)
- [M2E-UAV: A Benchmark and Analysis for Onboard Motion-on-Motion Event-Based Tiny UAV Detection](https://mlsanity.com/paper/2605.10496/) (2026)

## Built on by (most cited first, 1 of 1)

- [PointEvent: Rethinking Event-based Tiny Object Detection via Serialized Motion Evidence Accumulation](https://mlsanity.com/paper/2609.20066/) (2026)

## Abstract (arXiv)

Event cameras offer microsecond-level temporal resolution and high dynamic range for low-altitude UAV perception. However, long-range UAVs often produce sparse, fragmented, and noise-contaminated event responses, where one semantic target may appear as multiple spatially separated blobs. Direct blob-level asynchronous tracking therefore suffers from duplicate trajectories and unstable identities. We propose ASUMOT, a motion-consistency-based asynchronous UAV detection and tracking framework operating directly on raw events. ASUMOT models each UAV as a set of motion-consistent event blobs. A local motion-consistency estimator triggers reliable candidates, a lightweight multi-task verifier provides UAV confidence and motion-direction cues, and motion-consistency clustering aggregates fragmented blobs into identity-consistent UAV tracks. We also introduce ES-UAV, a high-definition event-level UAV benchmark with dense semantic annotations. Experiments on public UAV tracking data and ES-UAV show that ASUMOT improves the accuracy--efficiency trade-off while preserving asynchronous event processing. Code and Dataset will be released.