RhythmMamba: Fast Remote Physiological Measurement with Arbitrary Length Videos
<|reference_start|>RhythmMamba: Fast Remote Physiological Measurement with Arbitrary Length Videos: Remote photoplethysmography (rPPG) is a non-contact method for detecting physiological signals from facial videos, holding great potential in various applications such as healthcare, affective computing, and anti-spoofing. Existing deep learning methods struggle to address two core issues of rPPG simultaneously: extracting weak rPPG signals from video segments with large spatiotemporal redundancy and understanding the periodic patterns of rPPG among long contexts. This represents a trade-off between computational complexity and the ability to capture long-range dependencies, posing a challenge for rPPG that is suitable for deployment on mobile devices. Based on the in-depth exploration of Mamba's comprehension of spatial and temporal information, this paper introduces RhythmMamba, an end-to-end Mamba-based method that employs multi-temporal Mamba to constrain both periodic patterns and short-term trends, coupled with frequency domain feed-forward to enable Mamba to robustly understand the quasi-periodic patterns of rPPG. Extensive experiments show that RhythmMamba achieves state-of-the-art performance with reduced parameters and lower computational complexity. The proposed RhythmMamba can be applied to video segments of any length without performance degradation. The codes are available at https://github.com/zizheng-guo/RhythmMamba.<|reference_end|>
@article{zou2024rhythmmamba:,
    title={RhythmMamba: Fast Remote Physiological Measurement with Arbitrary Length
  Videos},
    author={Bochao Zou, Zizheng Guo, Xiaocheng Hu, Huimin Ma},
    journal={arXiv preprint arXiv:2404.06483},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06483},
    primaryClass={cs.CV}
}