Identifying Visitor's Paintings Appreciation for AI Audio Guide in Museums

Mari Saito, Takato Okudo, Takato Okudo, Makoto Yamada, Seiji Yamada, Seiji Yamada

2023

Abstract

This paper describes an application of machine learning for predicting whether a user is engaged in art appreciation to develop AI audio guide systems that can automatically control when guidance is provided. Although many studies on intelligent audio guides in museums have been done, there are few that have tried to develop AI audio guide systems that can begin to play audio guides automatically when visitors are engaged in art appreciation. In this paper, we determine the timing at which to begin an audio guide by classifying two classes, that is, whether the user is engaged in art appreciation or not, which is identified at the museum. We apply supervised machine learning for time-series data to the classification. We conducted experiments with participants in a real museum and collected labeled time-series data of participants heads’ postures and movements as training data. Then, we applied a classification learning algorithm for time-series data to predict when participants were involved in painting appreciation, executed model selection, and experimentally evaluated the models with the collected data. Since the results showed a good accuracy of over 82%, we confirmed that our machine learning-based approach to real-time identification of painting appreciation is promising for AI audio guide systems.

Paper Citation

in Harvard Style

Saito M., Okudo T., Yamada M. and Yamada S. (2023). Identifying Visitor's Paintings Appreciation for AI Audio Guide in Museums. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-623-1, pages 55-64. DOI: 10.5220/0011621500003393

in Bibtex Style

@conference{icaart23,
author={Mari Saito and Takato Okudo and Makoto Yamada and Seiji Yamada},
title={Identifying Visitor's Paintings Appreciation for AI Audio Guide in Museums},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2023},
pages={55-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011621500003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Identifying Visitor's Paintings Appreciation for AI Audio Guide in Museums
SN - 978-989-758-623-1
AU - Saito M.
AU - Okudo T.
AU - Yamada M.
AU - Yamada S.
PY - 2023
SP - 55
EP - 64
DO - 10.5220/0011621500003393