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Authors: Giovanni Pasqualino 1 ; Stefano Scafiti 1 ; Antonino Furnari 1 and Giovanni Farinella 2

Affiliations: 1 Department of Mathematics and Computer Science, University of Catania, Catania, Italy ; 2 Department of Mathematics and Computer Science, University of Catania, Catania, Italy, Cognitive Robotics and Social Sensing Laboratory, ICAR-CNR, Palermo, Italy

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): Egocentric (First Person) Vision, Localization, GPS, Multi-modal Data Fusion.

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 mem ory footprint of about 3MB and an inference speed of about 25ms. (More)

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Paper citation in several formats:
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; ISSN 2184-4321, pages 609-617. DOI: 10.5220/0009103906090617

@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},
issn={2184-4321},
}

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
IS - 2184-4321
AU - Pasqualino, G.
AU - Scafiti, S.
AU - Furnari, A.
AU - Farinella, G.
PY - 2020
SP - 609
EP - 617
DO - 10.5220/0009103906090617

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