计算机科学
接头(建筑物)
人工智能
保险丝(电气)
比例(比率)
噪音(视频)
模式识别(心理学)
计算机视觉
棱锥(几何)
频道(广播)
特征提取
人工神经网络
传感器融合
信号(编程语言)
运动(物理)
图像(数学)
电信
数学
建筑工程
物理
几何学
电气工程
量子力学
工程类
程序设计语言
作者
Changchen Zhao,Hongsheng Wang,Huiling Chen,Weiwei Shi,Yuanjing Feng
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2023-06-01
卷期号:33 (6): 2783-2797
被引量:37
标识
DOI:10.1109/tcsvt.2022.3227348
摘要
Remote photoplethysmography (rPPG) has been an active research topic in recent years. While most existing methods are focusing on eliminating motion artifacts in the raw traces obtained from single-scale region-of-interest (ROI), it is worth noting that there are some noise signals that cannot be effectively separated in single-scale space but can be separated more easily in multi-scale space. In this paper, we analyze the distribution of pulse signal and motion artifacts in different layers of a Gaussian pyramid. We propose a method that combines multi-scale analysis and neural network for pulse extraction in different scales, and a layer-wise attention mechanism to adaptively fuse the features according to signal strength. In addition, we propose spatial-temporal joint attention module and channel-temporal joint attention module to learn and exaggerate pulse features in the joint spaces, respectively. The proposed remote pulse extraction network is called Joint Attention and Multi-Scale fusion Network (JAMSNet). Extensive experiments have been conducted on two publicly available datasets and one self-collected dataset. The results show that the proposed JAMSNet shows better performance than state-of-the-art methods.
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