Image and Object Geo-Localization

IF 11.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Computer Vision Pub Date : 2023-11-18 DOI:10.1007/s11263-023-01942-3

Daniel Wilson, Xiaohan Zhang, Waqas Sultani, Safwan Wshah

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引用次数: 0

Abstract

The concept of geo-localization broadly refers to the process of determining an entity’s geographical location, typically in the form of Global Positioning System (GPS) coordinates. The entity of interest may be an image, a sequence of images, a video, a satellite image, or even objects visible within the image. Recently, massive datasets of GPS-tagged media have become available due to smartphones and the internet, and deep learning has risen to prominence and enhanced the performance capabilities of machine learning models. These developments have enabled the rise of image and object geo-localization, which has impacted a wide range of applications such as augmented reality, robotics, self-driving vehicles, road maintenance, and 3D reconstruction. This paper provides a comprehensive survey of visual geo-localization, which may involve either determining the location at which an image has been captured (image geo-localization) or geolocating objects within an image (object geo-localization). We will provide an in-depth study of visual geo-localization including a summary of popular algorithms, a description of proposed datasets, and an analysis of performance results to illustrate the current state of the field.

Abstract Image

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图像和目标地理定位

地理定位的概念广义上是指确定一个实体的地理位置的过程，通常以全球定位系统(GPS)坐标的形式出现。感兴趣的实体可以是图像、图像序列、视频、卫星图像或甚至图像中可见的对象。最近，由于智能手机和互联网的出现，大量带有gps标签的媒体数据集已经可用，深度学习已经崭露头角，并增强了机器学习模型的性能。这些发展促进了图像和物体地理定位的兴起，影响了增强现实、机器人、自动驾驶汽车、道路维护和3D重建等广泛的应用。本文提供了视觉地理定位的全面调查，这可能涉及确定图像被捕获的位置(图像地理定位)或对图像中的物体进行地理定位(物体地理定位)。我们将对视觉地理定位进行深入的研究，包括流行算法的总结，建议数据集的描述，以及性能结果的分析，以说明该领域的现状。

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来源期刊

International Journal of Computer Vision 工程技术-计算机：人工智能

CiteScore

29.80

自引率

2.10%

发文量

163

审稿时长

6 months

期刊介绍： The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.