# How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels

arXiv: https://arxiv.org/abs/2206.13673 · HTML: https://mlsanity.com/paper/2206.13673/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 52 references that are in the map.
- First posted on arXiv 28 Jun 2022.

## Builds on (most cited first, 24 of 24)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [Time-Ordered Recent Event (TORE) Volumes for Event Cameras](https://mlsanity.com/paper/2103.06108/) (2021)
- [Continuous-time Intensity Estimation Using Event Cameras](https://mlsanity.com/paper/1811.00386/) (2018)
- [Continuous-Time Visual-Inertial Odometry for Event Cameras](https://mlsanity.com/paper/1702.07389/) (2017)
- [HFirst: A Temporal Approach to Object Recognition](https://mlsanity.com/paper/1508.01176/) (2015)
- [Event-Based Angular Velocity Regression with Spiking Networks](https://mlsanity.com/paper/2003.02790/) (2020)
- [Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods](https://mlsanity.com/paper/1903.03305/) (2019)
- [Event-based visual place recognition with ensembles of temporal windows](https://mlsanity.com/paper/2006.02826/) (2020)
- [Star Tracking using an Event Camera](https://mlsanity.com/paper/1812.02895/) (2018)
- [Comparing Representations in Tracking for Event Camera-based SLAM](https://mlsanity.com/paper/2104.09887/) (2021)
- [Event-based Feature Extraction Using Adaptive Selection Thresholds](https://mlsanity.com/paper/1907.07853/) (2019)
- [Event-based attention and tracking on neuromorphic hardware](https://mlsanity.com/paper/1907.04060/) (2019)
- [Attention Mechanisms for Object Recognition with Event-Based Cameras](https://mlsanity.com/paper/1807.09480/) (2018)
- [Asynchronous Optimisation for Event-based Visual Odometry](https://mlsanity.com/paper/2203.01037/) (2022)
- [Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness](https://mlsanity.com/paper/2107.07707/) (2021)

## Built on by (most cited first, 18 of 18)

- [Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras](https://mlsanity.com/paper/2403.16425/) (2024)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [Unsupervised Quality Prediction for Improved Single-Frame and Weighted Sequential Visual Place Recognition](https://mlsanity.com/paper/2307.01464/) (2023)
- [Ensemble-Based Event Camera Place Recognition Under Varying Illumination](https://mlsanity.com/paper/2509.01968/) (2025)
- [Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition](https://mlsanity.com/paper/2604.03277/) (2026)
- [Spike-EVPR: Deep Spiking Residual Networks with SNN-Tailored Representations for Event-Based Visual Place Recognition](https://mlsanity.com/paper/2402.10476/) (2024)
- [Pushing the boundaries of event subsampling in event-based video classification using CNNs](https://mlsanity.com/paper/2409.08953/) (2024)
- [EVLoc: Event-based Visual Localization in LiDAR Maps via Event-Depth Registration](https://mlsanity.com/paper/2503.00167/) (2025)
- [Cross-modal Place Recognition in Image Databases using Event-based Sensors](https://mlsanity.com/paper/2307.01047/) (2023)
- [Low-Latency Scalable Streaming for Event-Based Vision](https://mlsanity.com/paper/2412.07889/) (2024)
- [EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition](https://mlsanity.com/paper/2603.05807/) (2026)
- [Scalable Event-Based Video Streaming for Machines with MoQ](https://mlsanity.com/paper/2508.15003/) (2025)
- [Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation](https://mlsanity.com/paper/2509.17287/) (2025)
- [Event-based high temporal resolution measurement of shock wave motion field](https://mlsanity.com/paper/2512.22474/) (2025)
- [Hybrid guided variational autoencoder for visual place recognition](https://mlsanity.com/paper/2601.09248/) (2026)
- [LEAR: Learning Edge-Aware Representations for Event-to-LiDAR Localization](https://mlsanity.com/paper/2603.01839/) (2026)
- [Multi-viewpoint Geo-localization with Event Cameras](https://mlsanity.com/paper/2609.21219/) (2026)
- [Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks](https://mlsanity.com/paper/2610.16022/) (2026)

## Abstract (arXiv)

Event cameras continue to attract interest due to desirable characteristics such as high dynamic range, low latency, virtually no motion blur, and high energy efficiency. One of the potential applications that would benefit from these characteristics lies in visual place recognition for robot localization, i.e. matching a query observation to the corresponding reference place in the database. In this letter, we explore the distinctiveness of event streams from a small subset of pixels (in the tens or hundreds). We demonstrate that the absolute difference in the number of events at those pixel locations accumulated into event frames can be sufficient for the place recognition task, when pixels that display large variations in the reference set are used. Using such sparse (over image coordinates) but varying (variance over the number of events per pixel location) pixels enables frequent and computationally cheap updates of the location estimates. Furthermore, when event frames contain a constant number of events, our method takes full advantage of the event-driven nature of the sensory stream and displays promising robustness to changes in velocity. We evaluate our proposed approach on the Brisbane-Event-VPR dataset in an outdoor driving scenario, as well as the newly contributed indoor QCR-Event-VPR dataset that was captured with a DAVIS346 camera mounted on a mobile robotic platform. Our results show that our approach achieves competitive performance when compared to several baseline methods on those datasets, and is particularly well suited for compute- and energy-constrained platforms such as interplanetary rovers.