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Proceedings Paper

Towards accurate camera geopositioning by image matching
Author(s): Raffaele Imbriaco; Clint Sebastian; Egor Bondarev; Peter H. N. de With
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Paper Abstract

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%.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411C (15 March 2019); doi: 10.1117/12.2522999
Show Author Affiliations
Raffaele Imbriaco, Eindhoven Univ. of Technology (Netherlands)
Clint Sebastian, Eindhoven Univ. of Technology (Netherlands)
Egor Bondarev, Eindhoven Univ. of Technology (Netherlands)
Peter H. N. de With, Eindhoven Univ. of Technology (Netherlands)

Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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