Visual Heart Rate Estimation with Convolutional Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00323980" target="_blank" >RIV/68407700:21230/18:00323980 - isvavai.cz</a>

Výsledek na webu

<a href="http://bmvc2018.org/contents/papers/0271.pdf" target="_blank" >http://bmvc2018.org/contents/papers/0271.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Visual Heart Rate Estimation with Convolutional Neural Network

Popis výsledku v původním jazyce

We propose a novel two-step convolutional neural network to estimate a heart rate from a sequence of facial images. The network is trained end-to-end by alternating op- timization and validated on three publicly available datasets yielding state-of-the-art results against three baseline methods. The network performs better by a 40% margin to the state-of-the-art method on a newly collected dataset. A challenging dataset of 204 fitness-themed videos is introduced. The dataset is designed to test the robustness of heart rate estimation methods to illumination changes and subject’s motion. 17 subjects perform 4 activities (talking, rowing, exercising on a stationary bike and an elliptical trainer) in 3 lighting setups. Each activity is captured by two RGB web-cameras, one is placed on a tripod, the other is attached to the fitness machine which vibrates significantly. Subject’s age ranges from 20 to 53 years, the mean heart rate is ~ 110, the standard deviation ~ 25.

Název v anglickém jazyce

Visual Heart Rate Estimation with Convolutional Neural Network

Popis výsledku anglicky

We propose a novel two-step convolutional neural network to estimate a heart rate from a sequence of facial images. The network is trained end-to-end by alternating op- timization and validated on three publicly available datasets yielding state-of-the-art results against three baseline methods. The network performs better by a 40% margin to the state-of-the-art method on a newly collected dataset. A challenging dataset of 204 fitness-themed videos is introduced. The dataset is designed to test the robustness of heart rate estimation methods to illumination changes and subject’s motion. 17 subjects perform 4 activities (talking, rowing, exercising on a stationary bike and an elliptical trainer) in 3 lighting setups. Each activity is captured by two RGB web-cameras, one is placed on a tripod, the other is attached to the fitness machine which vibrates significantly. Subject’s age ranges from 20 to 53 years, the mean heart rate is ~ 110, the standard deviation ~ 25.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů