YILDIZ-VPR dataset覆盖多环境下的视觉定位数据，助力真实场景下位置识别研究。
YILDIZ-VPR: A Novel Dataset with Dense Coverage Under Diverse Environmental Conditions for Visual Place Recognition
- 通过反复步行采集校园多视角视频，实现密集覆盖
- 包含昼夜、季节、天气变化下的丰富场景与传感器数据
- 适合研究长期视觉定位与鲁棒性评估的科研人员
视觉位置识别（VPR）旨在通过对比查询图像与一组地理参考图像来识别其位置。尽管已有众多VPR数据集，但从行人视角获取密集且多样化的视觉数据仍是重要需求。本文介绍YILDIZ-VPR，一个在伊斯坦布尔伊尔迪兹技术大学达武特帕萨校区通过重复步行路径采集的视觉地理定位数据集。数据集涵盖不同时间段、季节和天气条件下的户外场景，包含历史建筑、现代结构、道路、绿地和林地等丰富内容。所有视频均使用GoPro 9相机录制，并同步GPS传感器数据以提供帧级位置标签。此外，还包含陀螺仪、速度和温度等辅助传感器信息。凭借其密集覆盖与长期视觉变化特性，YILDIZ-VPR为研究基于图像与时间的视觉位置识别提供了真实户外环境下的实用资源。
原文摘要 · Abstract (English)
Visual Place Recognition (VPR) aims to recognize the location of a query image by comparing it with a set of geo-referenced images. Although many datasets have been proposed for VPR, collecting dense and diverse visual data from pedestrian-level viewpoints is still an important need. In this paper, we introduce YILDIZ-VPR, a visual geo-localization dataset collected through repeated walking traversals on the Davutpasa campus of Yildiz Technical University. The dataset includes outdoor scenes captured at different times of day, seasons, and weather conditions. It contains a wide range of visual content, including historical buildings, modern structures, roads, green areas, and wooded regions. Each video was recorded with a GoPro 9 camera and synchronized with GPS sensor data to provide location labels for the extracted frames. In addition to GPS coordinates, the dataset also includes auxiliary sensor information such as gyroscope, speed, and temperature data. With its dense coverage and long-term visual variability, YILDIZ-VPR provides a useful resource for studying image-based and temporal visual place recognition under realistic outdoor conditions.
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