Exploring Remote Physiological Signal Measurement under Dynamic Lighting Conditions at Night: Dataset, Experiment, and Analysis

• URL: http://arxiv.org/abs/2507.04306v1
• Date: Sun, 06 Jul 2025 09:16:08 GMT
• Title: Exploring Remote Physiological Signal Measurement under Dynamic Lighting Conditions at Night: Dataset, Experiment, and Analysis
• Authors: Zhipeng Li, Kegang Wang, Hanguang Xiao, Xingyue Liu, Feizhong Zhou, Jiaxin Jiang, Tianqi Liu,
• Abstract summary: We present and release a large-scale r dataset collected under dynamic lighting conditions at night, named DLCN.<n>The dataset comprises approximately 13 hours of video data and corresponding physiological signals from 98 participants, covering four representative nighttime lighting scenarios.<n>We provide a comprehensive analysis of the challenges faced by state-of-the-art r methods when applied to DLCN.
• Score: 7.679574342546723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote photoplethysmography (rPPG) is a non-contact technique for measuring human physiological signals. Due to its convenience and non-invasiveness, it has demonstrated broad application potential in areas such as health monitoring and emotion recognition. In recent years, the release of numerous public datasets has significantly advanced the performance of rPPG algorithms under ideal lighting conditions. However, the effectiveness of current rPPG methods in realistic nighttime scenarios with dynamic lighting variations remains largely unknown. Moreover, there is a severe lack of datasets specifically designed for such challenging environments, which has substantially hindered progress in this area of research. To address this gap, we present and release a large-scale rPPG dataset collected under dynamic lighting conditions at night, named DLCN. The dataset comprises approximately 13 hours of video data and corresponding synchronized physiological signals from 98 participants, covering four representative nighttime lighting scenarios. DLCN offers high diversity and realism, making it a valuable resource for evaluating algorithm robustness in complex conditions. Built upon the proposed Happy-rPPG Toolkit, we conduct extensive experiments and provide a comprehensive analysis of the challenges faced by state-of-the-art rPPG methods when applied to DLCN. The dataset and code are publicly available at https://github.com/dalaoplan/Happp-rPPG-Toolkit.

Related papers

• Reperio-rPPG: Relational Temporal Graph Neural Networks for Periodicity Learning in Remote Physiological Measurement [0.0]
  Remote photoplethys (rmography) is an emerging physiological sensing technique that leverages subtle color variations in facial videos to estimate vital signs such as heart rate and respiratory rate.<n>This non-invasive technique has gained traction across diverse domains, but its ability to capture fine-grained temporal dynamics under real-world conditions has been underexplored.<n>We propose Graph Reperio-r, a novel framework that strategically integrates a Transformer to effectively capture the periodic structure.
  arXiv  Detail & Related papers  (2025-11-08T09:41:34Z)
• LuxDiT: Lighting Estimation with Video Diffusion Transformer [66.60450792095901]
  Estimating scene lighting from a single image or video remains a longstanding challenge in computer vision and graphics.<n>We propose LuxDiT, a novel data-driven approach that fine-tunes a video diffusion transformer to generate HDR environment maps conditioned on visual input.
  arXiv  Detail & Related papers  (2025-09-03T19:59:20Z)
• HumanOLAT: A Large-Scale Dataset for Full-Body Human Relighting and Novel-View Synthesis [94.36785346337007]
  We introduce the HumanOLAT dataset, the first publicly accessible large-scale dataset of multi-view One-Light-at-a-Time (OLAT) captures of full-body humans.<n>The dataset includes HDR RGB frames under various illuminations, such as white light, environment maps, color gradients and fine-grained OLAT illuminations.<n>Our evaluations of state-of-the-art relighting and novel-view synthesis methods underscore both the dataset's value and the significant challenges still present in modeling complex human-centric appearance and lighting interactions.
  arXiv  Detail & Related papers  (2025-08-12T17:59:55Z)
• PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing [49.243031514520794]
  Large Language Models (LLMs) excel at capturing long-range signals due to their text-centric design.<n>PhysLLM achieves state-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.
  arXiv  Detail & Related papers  (2025-05-06T15:18:38Z)
• PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging [63.05435596565677]
  We propose PSDNorm that leverages Monge mapping and temporal context to normalize feature maps in deep learning models for signals.<n> PSDNorm achieves state-of-the-art performance on unseen left-out datasets while being 4-times more data-efficient than BatchNorm.
  arXiv  Detail & Related papers  (2025-03-06T16:20:25Z)
• ROVER: A Multi-Season Dataset for Visual SLAM [7.296917102476635]
  ROVER is a benchmark dataset for evaluating visual SLAM algorithms in diverse environmental conditions.<n>It covers 39 recordings across five outdoor locations, collected through all seasons and various lighting scenarios.<n>Results show that while stereo-inertial and RGBD configurations perform better under favorable lighting, most SLAM systems perform poorly in low-light and high-vegetation scenarios.
  arXiv  Detail & Related papers  (2024-12-03T15:34:00Z)
• LWIRPOSE: A novel LWIR Thermal Image Dataset and Benchmark [9.679771580702258]
  This dataset comprises over 2,400 high-quality LWIR (thermal) images. Each image is meticulously annotated with 2D human poses, offering a valuable resource for researchers and practitioners. We benchmark state-of-the-art pose estimation methods on the dataset to showcase its potential.
  arXiv  Detail & Related papers  (2024-04-16T01:49:35Z)
• LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment [59.320414108383055]
  We present LiveHPS, a novel single-LiDAR-based approach for scene-level human pose and shape estimation. We propose a huge human motion dataset, named FreeMotion, which is collected in various scenarios with diverse human poses.
  arXiv  Detail & Related papers  (2024-02-27T03:08:44Z)
• Implicit Event-RGBD Neural SLAM [54.74363487009845]
  Implicit neural SLAM has achieved remarkable progress recently. Existing methods face significant challenges in non-ideal scenarios. We propose EN-SLAM, the first event-RGBD implicit neural SLAM framework.
  arXiv  Detail & Related papers  (2023-11-18T08:48:58Z)
• 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)
• 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)
• LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds [58.402752909624716]
  Existing motion capture datasets are largely short-range and cannot yet fit the need of long-range applications. We propose LiDARHuman26M, a new human motion capture dataset captured by LiDAR at a much longer range to overcome this limitation. Our dataset also includes the ground truth human motions acquired by the IMU system and the synchronous RGB images.
  arXiv  Detail & Related papers  (2022-03-28T12:52:45Z)
• PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer [55.936527926778695]
  Recent deep learning approaches focus on mining subtle r clues using convolutional neural networks with limited-temporal receptive fields. In this paper, we propose the PhysFormer, an end-to-end video transformer based architecture.
  arXiv  Detail & Related papers  (2021-11-23T18:57:11Z)
• Artificial Dummies for Urban Dataset Augmentation [0.0]
  Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. This paper describes an augmentation method for controlled synthesis of urban scenes containing people. We demonstrate that the data generated by our DummyNet improve performance of several existing person detectors across various datasets.
  arXiv  Detail & Related papers  (2020-12-15T13:17:25Z)
• Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset [55.391532084304494]
  Driver Gaze in the Wild dataset contains 586 recordings, captured during different times of the day including evenings. Driver Gaze in the Wild dataset contains 338 subjects with an age range of 18-63 years.
  arXiv  Detail & Related papers  (2020-04-13T14:47:34Z)

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.