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Link to original content: https://doi.org/10.48550/arXiv.2405.02652
[2405.02652] Deep Pulse-Signal Magnification for remote Heart Rate Estimation in Compressed Videos
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2405.02652 (cs)
[Submitted on 4 May 2024 (v1), last revised 25 Jun 2024 (this version, v2)]

Title:Deep Pulse-Signal Magnification for remote Heart Rate Estimation in Compressed Videos

Authors:Joaquim Comas, Adria Ruiz, Federico Sukno
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Abstract:Recent advancements in data-driven approaches for remote photoplethysmography (rPPG) have significantly improved the accuracy of remote heart rate estimation. However, the performance of such approaches worsens considerably under video compression, which is nevertheless necessary to store and transmit video data efficiently. In this paper, we present a novel approach to address the impact of video compression on rPPG estimation, which leverages a pulse-signal magnification transformation to adapt compressed videos to an uncompressed data domain in which the rPPG signal is magnified. We validate the effectiveness of our model by exhaustive evaluations on two publicly available datasets, UCLA-rPPG and UBFC-rPPG, employing both intra- and cross-database performance at several compression rates. Additionally, we assess the robustness of our approach on two additional highly compressed and widely-used datasets, MAHNOB-HCI and COHFACE, which reveal outstanding heart rate estimation results.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2405.02652 [cs.CV]
  (or arXiv:2405.02652v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2405.02652

Submission history

From: Joaquim Comas Martínez [view email]
[v1] Sat, 4 May 2024 12:37:07 UTC (9,747 KB)
[v2] Tue, 25 Jun 2024 16:53:21 UTC (9,752 KB)
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