A 14.86k-parameter spectral attention model combining Chirp-Z zoom and unsupervised contrastive learning reaches near state-of-the-art cross-dataset heart rate accuracy under motion.
Deep Pulse-Signal Magnification for remote Heart Rate Estimation in Compressed Videos
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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.
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BeatFormer: Efficient motion-robust remote heart rate estimation through unsupervised spectral zoomed attention filters
A 14.86k-parameter spectral attention model combining Chirp-Z zoom and unsupervised contrastive learning reaches near state-of-the-art cross-dataset heart rate accuracy under motion.