Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience

• URL: http://arxiv.org/abs/2405.01230v1
• Date: Thu, 2 May 2024 12:21:51 GMT
• Title: Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience
• Authors: Nhi Nguyen, Le Nguyen, Honghan Li, Miguel Bordallo López, Constantino Álvarez Casado,
• Abstract summary: Video-based remote photoplethysmography (r) has emerged as a promising technology for non-contact vital sign monitoring. However, the accurate measurement of vital signs in real-world scenarios faces several challenges.
• Score: 1.6637373649145606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video-based remote photoplethysmography (rPPG) has emerged as a promising technology for non-contact vital sign monitoring, especially under controlled conditions. However, the accurate measurement of vital signs in real-world scenarios faces several challenges, including artifacts induced by videocodecs, low-light noise, degradation, low dynamic range, occlusions, and hardware and network constraints. In this article, we systematically investigate comprehensive investigate these issues, measuring their detrimental effects on the quality of rPPG measurements. Additionally, we propose practical strategies for mitigating these challenges to improve the dependability and resilience of video-based rPPG systems. We detail methods for effective biosignal recovery in the presence of network limitations and present denoising and inpainting techniques aimed at preserving video frame integrity. Through extensive evaluations and direct comparisons, we demonstrate the effectiveness of the approaches in enhancing rPPG measurements under challenging environments, contributing to the development of more reliable and effective remote vital sign monitoring technologies.

Related papers

• Towards Evaluating the Robustness of Visual State Space Models [63.14954591606638]
  Vision State Space Models (VSSMs) have demonstrated remarkable performance in visual perception tasks. However, their robustness under natural and adversarial perturbations remains a critical concern. We present a comprehensive evaluation of VSSMs' robustness under various perturbation scenarios.
  arXiv  Detail & Related papers  (2024-06-13T17:59:44Z)
• A Survey on Super Resolution for video Enhancement Using GAN [0.0]
  Recent developments in super-resolution image and video using deep learning algorithms such as Generative Adversarial Networks are covered. Advancements aim to increase the visual clarity and quality of low-resolution video, have tremendous potential in a variety of sectors ranging from surveillance technology to medical imaging. This collection delves into the wider field of Generative Adversarial Networks, exploring their principles, training approaches, and applications across a broad range of domains.
  arXiv  Detail & Related papers  (2023-12-27T08:41:38Z)
• AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System [52.93806509364342]
  This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. We introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features. We incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency.
  arXiv  Detail & Related papers  (2023-12-18T12:37:35Z)
• Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements [83.5820690348833]
  We present a framework for low-level vision tasks that does not require any external training data corpus. Our approach learns neural modules by optimizing over a corrupted sequence, leveraging the weights of the coherence-temporal test and statistics internal statistics.
  arXiv  Detail & Related papers  (2023-12-13T01:57:11Z)
• Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG [2.82697733014759]
  r (pg photoplethysmography) is a technology that measures and analyzes BVP (Blood Volume Pulse) by using the light absorption characteristics of hemoglobin captured through a camera. This study is to provide a framework to evaluate various r benchmarking techniques across a wide range of datasets for fair evaluation and comparison.
  arXiv  Detail & Related papers  (2023-07-24T09:35:47Z)
• Non-Contact Heart Rate Measurement from Deteriorated Videos [0.3149883354098941]
  Remote photoplethysmography (rmography) offers a state-of-the-art, non-contact methodology for estimating human pulse by analyzing facial videos. In this study, we apply image processing to intentionally degrade video quality, mimicking challenging conditions. Our results reveal a significant decrease in accuracy in the presence of these artifacts, prompting us to propose the application of restoration techniques.
  arXiv  Detail & Related papers  (2023-04-28T11:58:36Z)
• Image Enhancement for Remote Photoplethysmography in a Low-Light Environment [13.740047263242575]
  The accuracy of remote heart rate monitoring technology has been significantly improved. Despite the significant algorithmic advances, the performance of r algorithm can degrade in the long-term. Insufficient lighting in video capturing hurts quality of physiological signal. The proposed solution for r process is effective to detect and improve the signal-to-noise ratio and precision of the pulsatile signal.
  arXiv  Detail & Related papers  (2023-03-16T14:18:48Z)
• Fast Online Video Super-Resolution with Deformable Attention Pyramid [172.16491820970646]
  Video super-resolution (VSR) has many applications that pose strict causal, real-time, and latency constraints, including video streaming and TV. We propose a recurrent VSR architecture based on a deformable attention pyramid (DAP)
  arXiv  Detail & Related papers  (2022-02-03T17:49:04Z)
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
  We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
  arXiv  Detail & Related papers  (2020-11-05T11:34:12Z)
• Coherent Loss: A Generic Framework for Stable Video Segmentation [103.78087255807482]
  We investigate how a jittering artifact degrades the visual quality of video segmentation results. We propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts.
  arXiv  Detail & Related papers  (2020-10-25T10:48:28Z)
• Dynamic Error-bounded Lossy Compression (EBLC) to Reduce the Bandwidth Requirement for Real-time Vision-based Pedestrian Safety Applications [6.152873761869356]
  Video compression can impair real-time constraints of an ITS application, such as video-based real-time pedestrian detection. We develop a real-time error-bounded lossy compression (EBLC) strategy to dynamically change the video compression level depending on different environmental conditions.
  arXiv  Detail & Related papers  (2020-01-29T17:21:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.