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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 3/2017

28.03.2017 | Survey

State-of-the-art in visual geo-localization

verfasst von: Jan Brejcha, Martin Čadík

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2017

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Abstract

Large-scale visual geo-localization has recently gained a lot of attention in computer vision research and new methods are proposed steadily. However, surveys of visual geo-localization methods are rare and they focus mainly on city-scale localization methods. We present a comprehensive and balanced study of existing visual geo-localization domains, including city-scale, global approaches and methods for natural environments. We describe the methods to show their pros and cons, application domains, datasets, as well as evaluation techniques. We categorize the reviewed methods by two criteria. The first is the type of data the method uses for geo-location estimation. The second criterion is the target environment for which the method has been proposed and validated. Based on this categorization, we analyze important conditions that must be considered while solving geo-localization problems. Each category is in a different state of research—while city-scale image-based methods received a lot of attention, other categories such as natural environments using cross-domain data sources are still challenging problems under active research. Future research of large-scale visual geo-localization is discussed, primarily the challenging and new research category—geo-localization in natural environments.
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Fußnoten
1
Author’s note: in a reference database of geo-tagged images.
 
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Metadaten
Titel
State-of-the-art in visual geo-localization
verfasst von
Jan Brejcha
Martin Čadík
Publikationsdatum
28.03.2017
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 3/2017
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-017-0611-1

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Chain coding representation of voxel-based objects with enclosing, edging and intersecting trees

Theoretical Advances

A stochastic framework for K-SVD with applications on face recognition