Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos
Authors: Jianwei Li, Zitong Yu, Jingang Shi
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmark datasets (UBFC-r PPG, COHFACE and PURE) demonstrate the superior performance of the proposed method. |
| Researcher Affiliation | Academia | Jianwei Li1*, Zitong Yu2*, Jingang Shi1 1School of Software Engineering,Xi an Jiaotong University 2Great Bay University |
| Pseudocode | No | The paper describes the model architecture and its components (PFE, TFA) with figures and mathematical formulations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are available at https://github.com/LJWGIT/Arbitrary Resolution r PPG. |
| Open Datasets | Yes | Extensive experiments on three benchmark datasets (UBFC-r PPG, COHFACE and PURE) demonstrate the superior performance of the proposed method. |
| Dataset Splits | Yes | We first conduct experiments of r PPG-based HR measurement on three benchmark datasets with their original protocols and normal setting. |
| Hardware Specification | Yes | The proposed method is trained with batchsize 2 on RTX3090 GPU with Py Torch. ... Considering the deployment on edge devices, we provide a detailed analysis of complexity as well as the inference times on different devices (i.e., Nvidia RTX 3090 and Jetson AGX Orin) in Table 4. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The proposed method is trained with batchsize 2 on RTX3090 GPU with Py Torch. The Adam optimizer is used and the learning rate is set as 1e-4. The weight decay is 5e-5. |