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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
rPPG-Toolbox: Deep Remote PPG Toolbox (NeurIPS 2023)
| Date | Stars |
|---|---|
| 2026-07-30 | 1157 |
| 2026-08-10 | 1169 |
| 2026-08-18 | 1177 |
| 2026-08-21 | 1178 |
| 2026-08-25 | 1178 |
| 2026-08-26 | 1178 |
| 2026-08-27 | 1181 |
| 2026-08-31 | 1183 |
| 2026-09-01 | 1185 |
| 2026-09-03 | 1186 |
| 2026-09-04 | 1185 |
| 2026-09-05 | 1187 |
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| 2026-09-14 | 1201 |
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| 2026-09-17 | 1204 |
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| 2026-09-22 | 1207 |
| 2026-09-23 | 1209 |
| 2026-09-28 | 1210 |
| 2026-09-29 | 1213 |
| 2026-10-02 | 1214 |
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| 2026-10-05 | 1214 |
| 2026-10-07 | 1215 |
| 2026-10-08 | 1218 |
| 2026-10-09 | 1220 |
| 2026-10-10 | 1221 |
| 2026-10-12 | 1222 |
| 2026-10-13 | 1225 |
| 2026-10-14 | 1229 |
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| 2026-10-22 | 1235 |
| 2026-10-25 | 1236 |
| 2026-10-26 | 1236 |
| 2026-10-27 | 1237 |
| 2026-10-28 | 1240 |
| 2026-10-29 | 1240 |
| 2026-10-30 | 1242 |
| 2026-10-31 | 1243 |
| 2026-11-01 | 1244 |
| 2026-11-02 | 1246 |
| 2026-11-03 | 1247 |
| 2026-11-04 | 1249 |
Today
+2 stars today
This week
+9 stars this week
This month
+35 stars this month
Momentum
0.0
growth rate 0.73%/day
<p align="center"> :fire: Please remember to :star: this repo if you find it useful and cite our work if you end up using it in your work! :fire: </p> <p align="center"> :fire: If you have any questions or concerns, please create an <a href="https://github.com/ubicomplab/rPPG-Toolbox/issues">issue</a> :memo:! :fire: </p>  # :wave: Introduction **rPPG-Toolbox** is an open-source platform designed for camera-based physiological sensing, also known as remote photoplethysmography (rPPG).  rPPG-Toolbox not only benchmarks the **existing state-of-the-art neural and unsupervised methods**, but it also supports flexible and rapid development of your own algorithms.  # :notebook: Algorithms rPPG-Toolbox currently supports the following algorithms: * Traditional Unsupervised Algorithms - [Remote plethysmographic imaging using ambient light (GREEN)](https://pdfs.semanticscholar.org/7cb4/46d61a72f76e774b696515c55c92c7aa32b6.pdf?_gl=1*1q7hzyz*_ga*NTEzMzk5OTY3LjE2ODYxMDg1MjE.*_ga_H7P4ZT52H5*MTY4NjEwODUyMC4xLjAuMTY4NjEwODUyMS41OS4wLjA), by Verkruysse *et al.*, 2008 - [Advancements in noncontact multiparameter physiological measurements using a webcam (ICA)](https://affect.media.mit.edu/pdfs/11.Poh-etal-TBME.pdf), by Poh *et al.*, 2011 - [Robust pulse rate from chrominance-based rppg (CHROM)](https://ieeexplore.ieee.org/document/6523142), by Haan *et al.*, 2013 - [Local group invariance for heart rate estimation from face videos in the wild (LGI)](https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w27/Pilz_Local_Group_Invariance_CVPR_2018_paper.pdf), by Pilz *et al.*, 2018 - [Improved motion robustness of remote-PPG by using the blood volume pulse signature (PBV)](https://iopscience.iop.org/article/10.1088/0967-3334/35/9/1913), by Haan *et al.*, 2014 - [Algorithmic principles of remote ppg (POS)](https://ieeexplore.ieee.org/document/7565547), by Wang *et al.*, 2016 - [Face2PPG: An Unsupervised Pipeline for Blood Volume Pulse Extraction From Faces (OMIT)](https://ieeexplore.ieee.org/document/10227326), by Álvarez *et al.*, 2023 * Supervised Neural Algorithms - [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks (DeepPhys)](https://openaccess.thecvf.com/content_ECCV_2018/papers/Weixuan_Chen_DeepPhys_Video-Based_Physiological_ECCV_2018_paper.pdf), by Chen *et al.*, 2018 - [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks (PhysNet)](https://bmvc2019.org/wp-content/uploads/papers/0186-paper.pdf), by Yu *et al.*, 2019 - [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (TS-CAN)](https://papers.nips.cc/paper/2020/file/e1228be46de6a0234ac22ded31417bc7-Paper.pdf), by Liu *et al.*, 2020 - [EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Cardiac Measurement (EfficientPhys)](https://openaccess.thecvf.com/content/WACV2023/papers/Liu_EfficientPhys_Enabling_Simple_Fast_and_Accurate_Camera-Based_Cardiac_Measurement_WACV_2023_paper.pdf), by Liu *et al.*, 2023 - [BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements (BigSmall)](https://arxiv.org/abs/2303.11573), by Narayanswamy *et al.*, 2023 - [PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer (PhysFormer)](https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_PhysFormer_Facial_Video-Based_Physiological_Measurement_With_Temporal_Difference_Transformer_CVPR_2022_paper.pdf), by Yu *et al.*, 2022 - [iBVPNet: 3D-CNN architecture introduced in iBVP dataset paper](https://doi.org/10.3390/electronics13071334), by Joshi *et al.*, 2024 - [PhysMamba: Efficient Remote Physiological Measurement with SlowFast Temporal Difference Mamba](https://doi.org/10.48550/arXiv.2409.12031), by
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:02afeeb055038a77, topic:deep-learning
matched fp:02afeeb055038a77, topic:computer-vision
matched fp:02afeeb055038a77, topic:healthcare