# Towards Accurate Camera Geopositioning by Image Matching

## TL;DR

This paper introduces a fast and accurate camera geopositioning system that matches query images with panoramic database images using global descriptors, clustering, and outlier removal to improve speed and precision.

## Contribution

The work presents a novel combination of global image descriptors, clustering, and outlier removal algorithms for enhanced camera geopositioning accuracy and efficiency.

## Key findings

- Recall@5 exceeds 90% for panorama-to-panorama matching
- Clustering reduces computation time by ~50% with minimal recall loss (~3%)
- Median geopositioning error is reduced by up to 20%

## 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%.