Report
RhythmMamba: Fast Remote Physiological Measurement with Arbitrary Length Videos
| العنوان: | RhythmMamba: Fast Remote Physiological Measurement with Arbitrary Length Videos |
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| المؤلفون: | Zou, Bochao, Guo, Zizheng, Hu, Xiaocheng, Ma, Huimin |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: arXiv admin note: text overlap with arXiv:2402.12788 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.06483 |
| رقم الانضمام: | edsarx.2404.06483 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |