University of Oulu

X. Li et al., "The 2nd Challenge on Remote Physiological Signal Sensing (RePSS)," 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021, pp. 2404-2413, doi: 10.1109/ICCVW54120.2021.00273

The 2nd challenge on remote physiological signal sensing (RePSS)

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Author: Li, Xiaobai1; Sun, Haomiao2; Sun, Zhaodong1;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
2Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China
3Peng Cheng Laboratory, Shenzhen, China
4STARS team, INRIA, France
52Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202201041144
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2022-01-04
Description:

Abstract

Remote measurement of physiological signals from videos is an emerging topic. The topic draws great interest, but the lack of publicly available benchmark databases and a fair validation platform are hindering its further development. The RePSS Challenge is organized as an annual event for this concern. Here the 2nd RePSS is organized in conjunction with ICCV 2021. The 2nd RePSS contains two competition tracks. Track 1 is to measure inter-beat-intervals (IBI) from facial videos, which requires accurate measurement of each individual pulse peak. Track 2 is about respiration measurement from facial videos, as respiration is another important physiological index related to both health and emotional status. One new dataset is built and shared for Track 2. This paper presents an overview of the challenge, including data, protocol, results, and discussion. We highlighted the top-ranked solutions to provide insight for researchers, and we also outline future directions for this topic and this challenge.

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Series: IEEE International Conference on Computer Vision workshops
ISSN: 2473-9944
ISSN-E: 2473-9936
ISSN-L: 2473-9944
ISBN: 978-1-6654-0191-3
ISBN Print: 978-1-6654-0192-0
Pages: 2404 - 2413
DOI: 10.1109/ICCVW54120.2021.00273
OADOI: https://oadoi.org/10.1109/ICCVW54120.2021.00273
Host publication: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 11-17 Oct. 2021, Montreal, BC, Canada
Conference: IEEE/CVF International Conference on Computer Vision Workshops
Type of Publication: B3 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: Xiaobai Li and Guoying Zhao’s work were supported by National Natural Science Foundation of China (Grant 61772419), and Academy of Finland (Grant 316765 and 323287). Hu Han’s work was supported in part by Natural Science Foundation of China (Grant 61672496).
Academy of Finland Grant Number: 316765
323287
Detailed Information: 316765 (Academy of Finland Funding decision)
323287 (Academy of Finland Funding decision)
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