发表机构
Yildiz Technical University; BILGEM, TUBITAK; Istanbul University（伊尔迪兹技术大学; 土耳其科学技术研究理事会信息科学与安全研究所; 伊斯坦布尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出面向视觉地点识别的新型密集覆盖数据集YILDIZ-VPR，该数据集采集自伊兹密尔理工大学校区，含多样环境条件数据，可支持现实户外场景下的视觉地点识别研究。
AI 中文摘要
视觉地点识别（VPR）旨在通过将查询图像与一组地理参考图像进行比对，识别查询图像对应的位置。尽管已有许多针对VPR的数据集被提出，但从行人视角采集密集且多样的视觉数据仍然是一项重要需求。本文介绍YILDIZ-VPR，这是一个通过在伊兹密尔理工大学达武特帕萨校区反复步行采集的视觉地理定位数据集。该数据集包含在一天中的不同时段、不同季节及不同天气条件下拍摄的户外场景，涵盖历史建筑、现代建筑、道路、绿地及林区等丰富视觉内容。每个视频均使用GoPro 9相机录制，并与GPS传感器数据同步，为提取的帧提供位置标签。除GPS坐标外，数据集还包含陀螺仪、速度、温度数据等辅助传感器信息。凭借其密集覆盖范围和长期视觉变异性，YILDIZ-VPR为研究现实户外条件下基于图像的视觉地点识别及时序视觉地点识别提供了有用资源。
英文摘要
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.
