![[2602.22026] RGB-Event HyperGraph Prompt for Kilometer Marker Recognition based on Pre-trained Foundation Models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22026] RGB-Event HyperGraph Prompt for Kilometer Marker Recognition based on Pre-trained Foundation Models
Summary
This article presents a novel approach to Kilometer Marker Recognition (KMR) using RGB-event cameras, enhancing visual perception for autonomous metro systems in challenging environments.
Why It Matters
The integration of RGB-event cameras addresses critical challenges in visual perception under adverse conditions, improving the reliability of autonomous metro systems. This research contributes to the development of robust localization methods in GNSS-denied environments, which is essential for the future of public transportation and autonomous navigation.
Key Takeaways
- Proposes a new method for Kilometer Marker Recognition using RGB-event cameras.
- Introduces the EvMetro5K dataset, the first large-scale RGB-Event dataset for KMR.
- Demonstrates the effectiveness of the proposed approach through extensive experiments.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.22026 (cs) [Submitted on 25 Feb 2026] Title:RGB-Event HyperGraph Prompt for Kilometer Marker Recognition based on Pre-trained Foundation Models Authors:Xiaoyu Xian, Shiao Wang, Xiao Wang, Daxin Tian, Yan Tian View a PDF of the paper titled RGB-Event HyperGraph Prompt for Kilometer Marker Recognition based on Pre-trained Foundation Models, by Xiaoyu Xian and 4 other authors View PDF HTML (experimental) Abstract:Metro trains often operate in highly complex environments, characterized by illumination variations, high-speed motion, and adverse weather conditions. These factors pose significant challenges for visual perception systems, especially those relying solely on conventional RGB cameras. To tackle these difficulties, we explore the integration of event cameras into the perception system, leveraging their advantages in low-light conditions, high-speed scenarios, and low power consumption. Specifically, we focus on Kilometer Marker Recognition (KMR), a critical task for autonomous metro localization under GNSS-denied conditions. In this context, we propose a robust baseline method based on a pre-trained RGB OCR foundation model, enhanced through multi-modal adaptation. Furthermore, we construct the first large-scale RGB-Event dataset, EvMetro5K, containing 5,599 pairs of synchronized RGB-Event samples, split into 4,479 training and 1,120 testing samples. Extensive experiments on EvMetro5K and other wide...
