Localizing Visitors in Natural Sites Exploiting Modality Attention on Egocentric Images and GPS Data

Giovanni Pasqualino, Stefano Scafiti, Antonino Furnari, Giovanni Farinella

Abstract

Localizing the visitors of an outdoor natural site can be advantageous to study their behavior as well as to provide them information on where they are and what to visit in the site. Despite GPS can generally be used to perform outdoor localization, we show that this kind of signal is not always accurate enough in real-case scenarios. On the contrary, localization based on egocentric images can be more accurate but it generally results in more expensive computation. In this paper, we investigate how fusing image- and GPS-based predictions can allow to achieve efficient and accurate localization of the visitors of a natural site. Specifically, we compare different fusion techniques, including a modality attention approach which is shown to provide the best performances. Results point out that the proposed technique achieve promising results, allowing to obtain the performances of very deep models (e.g., DenseNet) with a less expensive architecture (e.g., SqueezeNet) which employ a memory footprint of about 3MB and an inference speed of about 25ms.

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Paper Citation


in Harvard Style

Pasqualino G., Scafiti S., Furnari A. and Farinella G. (2020). Localizing Visitors in Natural Sites Exploiting Modality Attention on Egocentric Images and GPS Data.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2, pages 609-617. DOI: 10.5220/0009103906090617


in Bibtex Style

@conference{visapp20,
author={Giovanni Pasqualino and Stefano Scafiti and Antonino Furnari and Giovanni Farinella},
title={Localizing Visitors in Natural Sites Exploiting Modality Attention on Egocentric Images and GPS Data},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={609-617},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009103906090617},
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 - Volume 5: VISAPP,
TI - Localizing Visitors in Natural Sites Exploiting Modality Attention on Egocentric Images and GPS Data
SN - 978-989-758-402-2
AU - Pasqualino G.
AU - Scafiti S.
AU - Furnari A.
AU - Farinella G.
PY - 2020
SP - 609
EP - 617
DO - 10.5220/0009103906090617