Towards accurate camera geopositioning by image matching

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In this work, we present a camera geopositioning system based on matching a query image against a database with panoramic images. For matching, our system uses memory vectors aggregated from global image descriptors based on convolutional features to facilitate fast searching in the database. To speed up searching, a clustering algorithm is used to balance geographical positioning and computation time. We refine the obtained position from the query image using a new outlier removal algorithm. The matching of the query image is obtained with a recall@5 larger than 90% for panorama-to-panorama matching. We cluster available panoramas from geographically adjacent locations into a single compact representation and observe computational gains of approximately 50% at the cost of only a small (approximately 3%) recall loss. Finally, we present a coordinate estimation algorithm that reduces the median geopositioning error by up to 20%.

Original languageEnglish
Title of host publicationEleventh International Conference on Machine Vision, ICMV 2018
EditorsAntanas Verikas, Dmitry P. Nikolaev, Petia Radeva, Jianhong Zhou
Place of PublicationBellingham
Number of pages8
ISBN (Electronic)9781510627482
Publication statusPublished - 1 Jan 2019
Event11th International Conference on Machine Vision, ICMV 2018 - Munich, Germany
Duration: 1 Nov 20183 Nov 2018
Conference number: 11

Publication series

NameProceedings of SPIE
ISSN (Print)0277-786X


Conference11th International Conference on Machine Vision, ICMV 2018
Abbreviated titleICMV


  • convolutional neural networks
  • geopositioning
  • Image matching
  • panoramic images
  • visual place recognition

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