Heart Rate Extraction Based on Near-Infrared Camera: Towards Driver State Monitoring

Zhang, Qi; Zhou, Yimin; Song, Shuang; Liang, Guoyuan; Ni, Haiyang

doi:10.1109/access.2018.2845390

Cited by 43 publications

(30 citation statements)

References 19 publications

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“…Another study by Zhang et al [145] used a webcam to monitor HR based on ICA during various driving situations. Other studies [146,147] have also applied the iPPG method to monitor the physiological parameters of drivers.…”

Section: Driver Monitoringmentioning

confidence: 99%

Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review

2019

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Techniques for noncontact measurement of vital signs using camera imaging technologies have been attracting increasing attention. For noncontact physiological assessments, computer vision-based methods appear to be an advantageous approach that could be robust, hygienic, reliable, safe, cost effective and suitable for long distance and long-term monitoring. In addition, video techniques allow measurements from multiple individuals opportunistically and simultaneously in groups. This paper aims to explore the progress of the technology from controlled clinical scenarios with fixed monitoring installations and controlled lighting, towards uncontrolled environments, crowds and moving sensor platforms. We focus on the diversity of applications and scenarios being studied in this topic. From this review it emerges that automatic multiple regions of interest (ROIs) selection, removal of noise artefacts caused by both illumination variations and motion artefacts, simultaneous multiple person monitoring, long distance detection, multi-camera fusion and accepted publicly available datasets are topics that still require research to enable the technology to mature into many real-world applications.

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Section: Driver Monitoringmentioning

confidence: 99%

Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review

2019

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“…It has been more and more closely related with people's daily lives and widely used in all fields of life. [1]- [3]. Figure 1 provides an example.…”

Section: Introductionmentioning

confidence: 99%

From Coarse to Fine (FC2F): A New Scheme of Colorizing Thermal Infrared Images

et al. 2020

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Colorization of the thermal infrared image is an unsolved problem because usually there is no one-to-one relationship between an object's color and its temperature. In this paper, we propose a new colorization scheme to address this problem. We first create a coarse colorization image that maintains cross-correlation between its RGB channels. Then the matrix of shift, rotation, and scaling between the coarse colorization image and a source natural color image is constructed. Finally, the transformations act on the coarse colorization image to obtain the resultant image, which has the appearance of the source image. Experiments demonstrate the effectiveness of the new scheme and show its superiority to other state-of-theart methods. The new scheme can be easily extended to the colorization of low illumination images and near-infrared images.

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“…Remote PPG has been far more exploited over the last years than BCG. Applications cover mixed reality [22], newborn health monitoring [23], physiological measurements of drivers [24], automatic skin detection and segmentation [19], and face anti-spoofing [25].…”

Section: Introductionmentioning

confidence: 99%

3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video

2019

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Remote pulse rate measurement from facial video has gained particular attention over the last few years. Research exhibits significant advancements and demonstrates that common video cameras correspond to reliable devices that can be employed to measure a large set of biomedical parameters without any contact with the subject. A new framework for measuring and mapping pulse rate from video is presented in this pilot study. The method, which relies on convolutional 3D networks, is fully automatic and does not require any special image preprocessing. In addition, the network ensures concurrent mapping by producing a prediction for each local group of pixels. A particular training procedure that employs only synthetic data is proposed. Preliminary results demonstrate that this convolutional 3D network can effectively extract pulse rate from video without the need for any processing of frames. The trained model was compared with other state-of-the-art methods on public data. Results exhibit significant agreement between estimated and ground-truth measurements: the root mean square error computed from pulse rate values assessed with the convolutional 3D network is equal to 8.64 bpm, which is superior to 10 bpm for the other state-of-the-art methods. The robustness of the method to natural motion and increases in performance correspond to the two main avenues that will be considered in future works.

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Heart Rate Extraction Based on Near-Infrared Camera: Towards Driver State Monitoring

Cited by 43 publications

References 19 publications

Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review

Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review

From Coarse to Fine (FC2F): A New Scheme of Colorizing Thermal Infrared Images

3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video

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