Cross-modal Place Recognition in Image Databases using Event-based
Sensors
- URL: http://arxiv.org/abs/2307.01047v1
- Date: Mon, 3 Jul 2023 14:24:04 GMT
- Title: Cross-modal Place Recognition in Image Databases using Event-based
Sensors
- Authors: Xiang Ji, Jiaxin Wei, Yifu Wang, Huiliang Shang and Laurent Kneip
- Abstract summary: We present the first cross-modal visual place recognition framework that is capable of retrieving regular images from a database given an event query.
Our method demonstrates promising results with respect to the state-of-the-art frame-based and event-based methods on the Brisbane-Event-VPR dataset.
- Score: 28.124708490967713
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Visual place recognition is an important problem towards global localization
in many robotics tasks. One of the biggest challenges is that it may suffer
from illumination or appearance changes in surrounding environments. Event
cameras are interesting alternatives to frame-based sensors as their high
dynamic range enables robust perception in difficult illumination conditions.
However, current event-based place recognition methods only rely on event
information, which restricts downstream applications of VPR. In this paper, we
present the first cross-modal visual place recognition framework that is
capable of retrieving regular images from a database given an event query. Our
method demonstrates promising results with respect to the state-of-the-art
frame-based and event-based methods on the Brisbane-Event-VPR dataset under
different scenarios. We also verify the effectiveness of the combination of
retrieval and classification, which can boost performance by a large margin.
Related papers
- CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding [52.67839570524888]
We present CEIA, an effective framework for open-world event-based understanding.
We leverage the rich event-image datasets to learn an event embedding space aligned with the image space of CLIP.
CEIA offers two distinct advantages. First, it allows us to take full advantage of the existing event-image datasets to make up the shortage of large-scale event-text datasets.
arXiv Detail & Related papers (2024-07-09T07:26:15Z) - Research, Applications and Prospects of Event-Based Pedestrian Detection: A Survey [10.494414329120909]
Event-based cameras, inspired by the biological retina, have evolved into cutting-edge sensors distinguished by their minimal power requirements, negligible latency, superior temporal resolution, and expansive dynamic range.
Event-based cameras address limitations by eschewing extraneous data transmissions and obviating motion blur in high-speed imaging scenarios.
This paper offers an exhaustive review of research and applications particularly in the autonomous driving context.
arXiv Detail & Related papers (2024-07-05T06:17:00Z) - Relating Events and Frames Based on Self-Supervised Learning and
Uncorrelated Conditioning for Unsupervised Domain Adaptation [23.871860648919593]
Event-based cameras provide accurate and high temporal resolution measurements for performing computer vision tasks.
Despite their advantages, utilizing deep learning for event-based vision encounters a significant obstacle due to the scarcity of annotated data.
We propose a new algorithm tailored for adapting a deep neural network trained on annotated frame-based data to generalize well on event-based unannotated data.
arXiv Detail & Related papers (2024-01-02T05:10:08Z) - EventTransAct: A video transformer-based framework for Event-camera
based action recognition [52.537021302246664]
Event cameras offer new opportunities compared to standard action recognition in RGB videos.
In this study, we employ a computationally efficient model, namely the video transformer network (VTN), which initially acquires spatial embeddings per event-frame.
In order to better adopt the VTN for the sparse and fine-grained nature of event data, we design Event-Contrastive Loss ($mathcalL_EC$) and event-specific augmentations.
arXiv Detail & Related papers (2023-08-25T23:51:07Z) - Event-based Simultaneous Localization and Mapping: A Comprehensive Survey [52.73728442921428]
Review of event-based vSLAM algorithms that exploit the benefits of asynchronous and irregular event streams for localization and mapping tasks.
Paper categorizes event-based vSLAM methods into four main categories: feature-based, direct, motion-compensation, and deep learning methods.
arXiv Detail & Related papers (2023-04-19T16:21:14Z) - Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks [55.81577205593956]
Event cameras are bio-inspired sensors that capture the per-pixel intensity changes asynchronously.
Deep learning (DL) has been brought to this emerging field and inspired active research endeavors in mining its potential.
arXiv Detail & Related papers (2023-02-17T14:19:28Z) - How Many Events do You Need? Event-based Visual Place Recognition Using
Sparse But Varying Pixels [29.6328152991222]
One of the potential applications of event camera research lies in visual place recognition for robot localization.
We show 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.
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.
arXiv Detail & Related papers (2022-06-28T00:24:12Z) - Bridging the Gap between Events and Frames through Unsupervised Domain
Adaptation [57.22705137545853]
We propose a task transfer method that allows models to be trained directly with labeled images and unlabeled event data.
We leverage the generative event model to split event features into content and motion features.
Our approach unlocks the vast amount of existing image datasets for the training of event-based neural networks.
arXiv Detail & Related papers (2021-09-06T17:31:37Z) - Learning to Detect Objects with a 1 Megapixel Event Camera [14.949946376335305]
Event cameras encode visual information with high temporal precision, low data-rate, and high-dynamic range.
Due to the novelty of the field, the performance of event-based systems on many vision tasks is still lower compared to conventional frame-based solutions.
arXiv Detail & Related papers (2020-09-28T16:03:59Z) - Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem
Formulation [53.850686395708905]
Event-based cameras record an asynchronous stream of per-pixel brightness changes.
In this paper, we focus on single-layer architectures for representation learning from event data.
We show improvements of up to 9 % in the recognition accuracy compared to the state-of-the-art methods.
arXiv Detail & Related papers (2020-09-23T10:40:03Z) - City-Scale Visual Place Recognition with Deep Local Features Based on
Multi-Scale Ordered VLAD Pooling [5.274399407597545]
We present a fully-automated system for place recognition at a city-scale based on content-based image retrieval.
Firstly, we take a comprehensive analysis of visual place recognition and sketch out the unique challenges of the task.
Next, we propose yet a simple pooling approach on top of convolutional neural network activations to embed the spatial information into the image representation vector.
arXiv Detail & Related papers (2020-09-19T15:21:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.