Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features with Transformers and Recursive Convolutional Models
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021(2021)
SiMa Technol Inc | Intel Corp | Gdansk Univ Technol
Abstract
Non-contact evaluation of vital signs has been becoming increasingly important, especially in light of the COVID-19 pandemic, which is causing the whole world to examine people’s interactions in public places at a scale never seen before. However, evaluating one’s vital signs can be a relatively complex procedure, which requires both time and physical contact between examiner and examinee. These requirements limit the number of people who can be efficiently checked, either due to the medical station throughput, patients’ remote locations or the need for social distancing. This study is a first step to increasing the accuracy of computer vision-based respiratory rate estimation by transferring texture information from images acquired in different domains. Experiments conducted with two deep neural network topologies, a recursive convolutional model and transformers, proved their robustness in the analyzed scenario by reducing estimation error by 50% compared to low resolution sequences. All resources used in this research, including links to the dataset and code, have been made publicly available.
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Key words
examiner,examinee,medical station throughput,patients,social distancing,computer vision-based respiratory rate estimation,deep neural network topologies,recursive convolutional model,transformers,estimation error,low resolution sequences,improving accuracy,high resolution features,noncontact evaluation,vital signs,COVID-19 pandemic,public places,relatively complex procedure,physical contact
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