Abstract
We present a novel approach to geolocalising panoramic images on a 2-D cartographic map based on learning a low dimensional embedded space, which allows a comparison between an image captured at a location and local neighbourhoods of the map. The representation is not sufficiently discriminatory to allow localisation from a single image, but when concatenated along a route, localisation converges quickly, with over 90% accuracy being achieved for routes of around 200m in length when using Google Street View and Open Street Map data. The method generalises a previous fixed semantic feature based approach and achieves significantly higher localisation accuracy and faster convergence.
Original language | English |
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Number of pages | 17 |
Publication status | Published - 28 Aug 2020 |
Event | 16th European Conference on Computer Vision - Online Duration: 23 Aug 2020 → 28 Aug 2020 https://eccv2020.eu |
Conference
Conference | 16th European Conference on Computer Vision |
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Abbreviated title | ECCV20 |
Period | 23/08/20 → 28/08/20 |
Internet address |
Keywords
- Geolocalisation
- image-map embeddings
- cross domain localisation
- representation learning
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Dive into the research topics of 'You Are Here: Geolocation by Embedding Maps and Images'. Together they form a unique fingerprint.Profiles
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Professor Andrew Calway
- School of Computer Science - Professor of Computer Vision
- Visual Information Laboratory
Person: Academic , Member