# Event-based visual place recognition with ensembles of temporal windows

arXiv: https://arxiv.org/abs/2006.02826 · HTML: https://mlsanity.com/paper/2006.02826/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 30 of 36)

- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [GET: Group Event Transformer for Event-Based Vision](https://mlsanity.com/paper/2310.02642/) (2023)
- [Scene Adaptive Sparse Transformer for Event-based Object Detection](https://mlsanity.com/paper/2404.01882/) (2024)
- [ViViD++: Vision for Visibility Dataset](https://mlsanity.com/paper/2204.06183/) (2022)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels](https://mlsanity.com/paper/2206.13673/) (2022)
- [Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception](https://mlsanity.com/paper/2401.13853/) (2024)
- [Place recognition survey: An update on deep learning approaches](https://mlsanity.com/paper/2106.10458/) (2021)
- [FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events](https://mlsanity.com/paper/2211.12244/) (2022)
- [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)
- [NYC-Event-VPR: A Large-Scale High-Resolution Event-Based Visual Place Recognition Dataset in Dense Urban Environments](https://mlsanity.com/paper/2410.21615/) (2024)
- [Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition](https://mlsanity.com/paper/2112.04701/) (2021)
- [Privacy-Preserving Visual Localization with Event Cameras](https://mlsanity.com/paper/2212.03177/) (2022)
- [CoSEC: A Coaxial Stereo Event Camera Dataset for Autonomous Driving](https://mlsanity.com/paper/2408.08500/) (2024)
- [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)
- [EchoVPR: Echo State Networks for Visual Place Recognition](https://mlsanity.com/paper/2110.05572/) (2021)
- [Spike-EVPR: Deep Spiking Residual Networks with SNN-Tailored Representations for Event-Based Visual Place Recognition](https://mlsanity.com/paper/2402.10476/) (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)
- [CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage Refinement](https://mlsanity.com/paper/2311.11604/) (2023)
- [A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition](https://mlsanity.com/paper/2412.06153/) (2024)
- [EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition](https://mlsanity.com/paper/2603.05807/) (2026)
- [Bio-inspired robot perception coupled with robot-modeled human perception](https://mlsanity.com/paper/2109.00097/) (2021)
- [Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation](https://mlsanity.com/paper/2509.17287/) (2025)
- [Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition](https://mlsanity.com/paper/2510.17739/) (2025)
- [Hybrid guided variational autoencoder for visual place recognition](https://mlsanity.com/paper/2601.09248/) (2026)

## Abstract (arXiv)

Event cameras are bio-inspired sensors capable of providing a continuous stream of events with low latency and high dynamic range. As a single event only carries limited information about the brightness change at a particular pixel, events are commonly accumulated into spatio-temporal windows for further processing. However, the optimal window length varies depending on the scene, camera motion, the task being performed, and other factors. In this research, we develop a novel ensemble-based scheme for combining temporal windows of varying lengths that are processed in parallel. For applications where the increased computational requirements of this approach are not practical, we also introduce a new "approximate" ensemble scheme that achieves significant computational efficiencies without unduly compromising the original performance gains provided by the ensemble approach. We demonstrate our ensemble scheme on the visual place recognition (VPR) task, introducing a new Brisbane-Event-VPR dataset with annotated recordings captured using a DAVIS346 color event camera. We show that our proposed ensemble scheme significantly outperforms all the single-window baselines and conventional model-based ensembles, irrespective of the image reconstruction and feature extraction methods used in the VPR pipeline, and evaluate which ensemble combination technique performs best. These results demonstrate the significant benefits of ensemble schemes for event camera processing in the VPR domain and may have relevance to other related processes, including feature tracking, visual-inertial odometry, and steering prediction in driving.