Domain Generalization for Activity Recognition: Learn from Visible, Infer with Thermal

Yannick Zoetgnande; Jean Louis Dillenseger

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Paper

Domain Generalization for Activity Recognition: Learn from Visible, Infer with Thermal

Topics: Advanced Learning Methods; Deep Learning and Neural Networks; Image and Video Analysis and Understanding; Machine Learning Methods; Motion Tracking and Action Recognition

In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 722-729, 2022

Authors: Yannick Zoetgnande ¹ and Jean Louis Dillenseger ²

Affiliations: ¹ Independent Researcher, France ; ² Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France

Keyword(s): Fall Detection, Activity Recognition, Domain Generalization, Thermal Images.

Abstract: We proposed a solution based on I3D and optical flow to learn common characteristics between thermal and visible videos. For this purpose we proposed a new database to evaluate our solution. The new model comprises an optical flow extractor; a feature extractor based on I3D, a domain classifier, and an activity recognition classifier. We learn invariant characteristics computed from the optical flow. We have simulated several source domains, and we have shown that it is possible to obtain excellent results on a modality that was not used during the training. Such techniques can be used when there is only one source and one target domain.

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Paper citation in several formats:

Zoetgnande, Y. and Dillenseger, J. (2022). Domain Generalization for Activity Recognition: Learn from Visible, Infer with Thermal. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-549-4; ISSN 2184-4313, SciTePress, pages 722-729. DOI: 10.5220/0010906300003122

@conference{icpram22,
author={Yannick Zoetgnande. and Jean Louis Dillenseger.},
title={Domain Generalization for Activity Recognition: Learn from Visible, Infer with Thermal},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2022},
pages={722-729},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010906300003122},
isbn={978-989-758-549-4},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Domain Generalization for Activity Recognition: Learn from Visible, Infer with Thermal
SN - 978-989-758-549-4
IS - 2184-4313
AU - Zoetgnande, Y.
AU - Dillenseger, J.
PY - 2022
SP - 722
EP - 729
DO - 10.5220/0010906300003122
PB - SciTePress