# FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras

arXiv: https://arxiv.org/abs/1906.10925 · HTML: https://mlsanity.com/paper/1906.10925/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 26 of 26)

- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [LuvHarris: A Practical Corner Detector for Event-cameras](https://mlsanity.com/paper/2105.11443/) (2021)
- [eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-](https://mlsanity.com/paper/2207.09108/) (2022)
- [Stereo Event-based, 6-DOF Pose Tracking for Uncooperative Spacecraft](https://mlsanity.com/paper/2503.12732/) (2025)
- [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://mlsanity.com/paper/2304.09793/) (2023)
- [ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras](https://mlsanity.com/paper/2410.09374/) (2024)
- [Asynchronous Blob Tracker for Event Cameras](https://mlsanity.com/paper/2307.10593/) (2023)
- [Implicit Event-RGBD Neural SLAM](https://mlsanity.com/paper/2311.11013/) (2023)
- [IMU-Aided Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2405.04071/) (2024)
- [Tracking Any Point with Frame-Event Fusion Network at High Frame Rate](https://mlsanity.com/paper/2409.11953/) (2024)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [Probabilistic Approach for Detection of High-Frequency Periodic Signals using an Event Camera](https://mlsanity.com/paper/2205.04691/) (2022)
- [Event-based Structure-from-Orbit](https://mlsanity.com/paper/2405.06216/) (2024)
- [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://mlsanity.com/paper/2504.08588/) (2025)
- [Continuous-Time Gaussian Process Motion-Compensation for Event-vision Pattern Tracking with Distance Fields](https://mlsanity.com/paper/2303.02672/) (2023)
- [Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling](https://mlsanity.com/paper/2505.21187/) (2025)
- [ASAP: Adaptive Scheme for Asynchronous Processing of Event-based Vision Algorithms](https://mlsanity.com/paper/2209.08597/) (2022)
- [EI-Nexus: Towards Unmediated and Flexible Inter-Modality Local Feature Extraction and Matching for Event-Image Data](https://mlsanity.com/paper/2410.21743/) (2024)
- [AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression](https://mlsanity.com/paper/2411.12175/) (2024)
- [On the Theory of Bias Tuning in Event Cameras](https://mlsanity.com/paper/2501.18788/) (2025)
- [e-ACJ: Accurate Junction Extraction For Event Cameras](https://mlsanity.com/paper/2101.11251/) (2021)
- [SE-Harris and eSUSAN: Asynchronous Event-Based Corner Detection Using Megapixel Resolution CeleX-V Camera](https://mlsanity.com/paper/2105.00480/) (2021)
- [ANMS: Asynchronous Non-Maximum Suppression in Event Stream](https://mlsanity.com/paper/2303.10575/) (2023)
- [Event-ECC: Asynchronous Tracking of Events with Continuous Optimization](https://mlsanity.com/paper/2409.14564/) (2024)
- [Event Driven Clustering Algorithm](https://mlsanity.com/paper/2602.00115/) (2026)
- [Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues](https://mlsanity.com/paper/2608.11075/) (2026)

## Abstract (arXiv)

Recently, the emerging bio-inspired event cameras have demonstrated potentials for a wide range of robotic applications in dynamic environments. In this paper, we propose a novel fast and asynchronous event-based corner detection method which is called FA-Harris. FA-Harris consists of several components, including an event filter, a Global Surface of Active Events (G-SAE) maintaining unit, a corner candidate selecting unit, and a corner candidate refining unit. The proposed G-SAE maintenance algorithm and corner candidate selection algorithm greatly enhance the real-time performance for corner detection, while the corner candidate refinement algorithm maintains the accuracy of performance by using an improved event-based Harris detector. Additionally, FA-Harris does not require artificially synthesized event-frames and can operate on asynchronous events directly. We implement the proposed method in C++ and evaluate it on public Event Camera Datasets. The results show that our method achieves approximately 8x speed-up when compared with previously reported event-based Harris detector, and with no compromise on the accuracy of performance.