Location Estimation of an Urban Scene using Computer Vision Techniques

Paul Gordan, Hanniel Boros, Ion Giosan

2020

Abstract

The process of adding the geographical identification data to an image is called geotagging and is important for a range of applications starting from tourism to law enforcement agencies. The most convenient way of adding location metadata to an image is GPS geotagging. This article presents an alternative way of adding the approximate location metadata to an urban scene image by finding similar images in a dataset of geotagged images. The matching is done by extracting the image features and descriptors and matching them. The dataset consists in geotagged 360◦ panoramic images. We explored three methods of matching the images, each one being an iteration of the previous method. The first method used only feature detection and matching using AKAZE and FLANN, the second method performed image segmentation to provide a mask for extracting features and descriptors only from buildings and the third method preprocessed the dataset to obtain better accuracy. We managed to improve the accuracy of the system by 25%. Following the in-depth analysis of the results we will present the results as well as future improvements.

Paper Citation

in Harvard Style

Gordan P., Boros H. and Giosan I. (2020). Location Estimation of an Urban Scene using Computer Vision Techniques. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 268-275. DOI: 10.5220/0008949102680275

in Bibtex Style

@conference{visapp20,
author={Paul Gordan and Hanniel Boros and Ion Giosan},
title={Location Estimation of an Urban Scene using Computer Vision Techniques},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={268-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008949102680275},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Location Estimation of an Urban Scene using Computer Vision Techniques
SN - 978-989-758-402-2
AU - Gordan P.
AU - Boros H.
AU - Giosan I.
PY - 2020
SP - 268
EP - 275
DO - 10.5220/0008949102680275
PB - SciTePress