The 3rd Vision-based Remote Physiological Signal Sensing (RePSS) Challenge & Workshop
Sun, Zhaodong; Li, Xiaobai; Han, Hu; Tang, Jiyang; Ying, Chenhang; Ge, Jieyi; Dantcheva, Antitza; Shan, Shiguang; Zhao, Guoying (2024-08-05)
R. Piskac c/o Redaktion Sun SITE, Informatik V, RWTH Aachen
05.08.2024
Sun, Zhaodong; Li, Xiaobai; Han, Hu; Tang, Jiyang; Ying, Chenhang; Ge, Jieyi; Dantcheva, Antitza; Shan, Shiguang & Zhao, Guoying (2024) The 3rd Vision-based Remote Physiological Signal Sensing (RePSS) Challenge & Workshop, In (eds. Li, Xiaobai et al.) CEUR Workshop Proceedings, CEUR workshop proceedings, 3750, pp. 1-14
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© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202409175909
https://urn.fi/URN:NBN:fi:oulu-202409175909
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Abstract
The remote measurement of physiological signals from video recordings is a topic of growing interest. Despite its potential, progress in this field is being impeded by the absence of publicly available benchmark databases and a standardized validation platform. To address these issues, the RePSS Challenge is held annually. The 3rd RePSS Challenge is being conducted alongside IJCAI 2024 and features two competition tracks. Track 1 focuses on self-supervised learning for heart rate measurement using unlabeled facial videos, while Track 2 tackles the more complex task of measuring blood pressure from facial videos. This paper provides an overview of the challenge, detailing the data, protocols, analysis of results, and discussions. We highlight the top-performing solutions to offer insights for researchers and outline future directions for this field and the challenge itself.
The remote measurement of physiological signals from video recordings is a topic of growing interest. Despite its potential, progress in this field is being impeded by the absence of publicly available benchmark databases and a standardized validation platform. To address these issues, the RePSS Challenge is held annually. The 3rd RePSS Challenge is being conducted alongside IJCAI 2024 and features two competition tracks. Track 1 focuses on self-supervised learning for heart rate measurement using unlabeled facial videos, while Track 2 tackles the more complex task of measuring blood pressure from facial videos. This paper provides an overview of the challenge, detailing the data, protocols, analysis of results, and discussions. We highlight the top-performing solutions to offer insights for researchers and outline future directions for this field and the challenge itself.
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