RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement
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arXiv
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| Format: | Preprint |
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2024
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| author | Zou, Bochao Guo, Zizheng Hu, Xiaocheng Ma, Huimin |
| author_facet | Zou, Bochao Guo, Zizheng Hu, Xiaocheng Ma, Huimin |
| contents | Remote photoplethysmography (rPPG) is a method for non-contact measurement of 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: understanding the periodic pattern of rPPG among long contexts and addressing large spatiotemporal redundancy in video segments. These represent a trade-off between computational complexity and the ability to capture long-range dependencies. In this paper, we introduce RhythmMamba, a state space model-based method that captures long-range dependencies while maintaining linear complexity. By viewing rPPG as a time series task through the proposed frame stem, the periodic variations in pulse waves are modeled as state transitions. Additionally, we design multi-temporal constraint and frequency domain feed-forward, both aligned with the characteristics of rPPG time series, to improve the learning capacity of Mamba for rPPG signals. Extensive experiments show that RhythmMamba achieves state-of-the-art performance with 319% throughput and 23% peak GPU memory. The codes are available at https://github.com/zizheng-guo/RhythmMamba. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06483 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement Zou, Bochao Guo, Zizheng Hu, Xiaocheng Ma, Huimin Computer Vision and Pattern Recognition Remote photoplethysmography (rPPG) is a method for non-contact measurement of 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: understanding the periodic pattern of rPPG among long contexts and addressing large spatiotemporal redundancy in video segments. These represent a trade-off between computational complexity and the ability to capture long-range dependencies. In this paper, we introduce RhythmMamba, a state space model-based method that captures long-range dependencies while maintaining linear complexity. By viewing rPPG as a time series task through the proposed frame stem, the periodic variations in pulse waves are modeled as state transitions. Additionally, we design multi-temporal constraint and frequency domain feed-forward, both aligned with the characteristics of rPPG time series, to improve the learning capacity of Mamba for rPPG signals. Extensive experiments show that RhythmMamba achieves state-of-the-art performance with 319% throughput and 23% peak GPU memory. The codes are available at https://github.com/zizheng-guo/RhythmMamba. |
| title | RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.06483 |