Resource Optimization of the Eulerian Video Magnification Algorithm Towards an Embedded Architecture

Ki Sung Lim; Eduardo Moya-Bello; Luis Chavarria-Zamora

doi:10.1109/URUCON53396.2021.9647386

Resource Optimization of the Eulerian Video Magnification Algorithm Towards an Embedded Architecture

Ki Sung Lim
, Eduardo Moya-Bello
, Luis Chavarria-Zamora

Escuela de Ingeniería en Computadores

Costa Rica Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The use of algorithms like the Eulerian Video Magnification (EVM) could make a low-cost alternative for monitoring vital signs. A remote, non-invasive patient monitoring is advantageous, and it is possible using EVM. However, computational resources may be optimized to be executed in memory and power efficient computer architectures. Current implementations of the EVM lack of parallelization and its memory management can be improved using low-level languages. Our project seeks to optimize the Magnification algorithm to detect vital signs like respiratory and heart rate, non-invasive and more efficiently. According to our tests, both execution times and memory use are improved. The obtained results show an average improvement of 434% in execution times, with a maximum speedup of 746%. In addition, the implemented algorithm utilizes 200 MB less memory in average.

Original language	English
Title of host publication	2021 IEEE URUCON, URUCON 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	576-579
Number of pages	4
ISBN (Electronic)	9781665424431
DOIs	https://doi.org/10.1109/URUCON53396.2021.9647386
State	Published - 2021
Event	2021 IEEE URUCON, URUCON 2021 - Montevideo, Uruguay Duration: 24 Nov 2021 → 26 Nov 2021

Publication series

Name	2021 IEEE URUCON, URUCON 2021

Conference

Conference	2021 IEEE URUCON, URUCON 2021
Country/Territory	Uruguay
City	Montevideo
Period	24/11/21 → 26/11/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Computational resources
Eulerian Video Magnification
Parallelization
Video processing
Vital signs

Access to Document

10.1109/URUCON53396.2021.9647386

Cite this

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title = "Resource Optimization of the Eulerian Video Magnification Algorithm Towards an Embedded Architecture",

abstract = "The use of algorithms like the Eulerian Video Magnification (EVM) could make a low-cost alternative for monitoring vital signs. A remote, non-invasive patient monitoring is advantageous, and it is possible using EVM. However, computational resources may be optimized to be executed in memory and power efficient computer architectures. Current implementations of the EVM lack of parallelization and its memory management can be improved using low-level languages. Our project seeks to optimize the Magnification algorithm to detect vital signs like respiratory and heart rate, non-invasive and more efficiently. According to our tests, both execution times and memory use are improved. The obtained results show an average improvement of 434\% in execution times, with a maximum speedup of 746\%. In addition, the implemented algorithm utilizes 200 MB less memory in average.",

keywords = "Computational resources, Eulerian Video Magnification, Parallelization, Video processing, Vital signs",

author = "Lim, \{Ki Sung\} and Eduardo Moya-Bello and Luis Chavarria-Zamora",

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year = "2021",

doi = "10.1109/URUCON53396.2021.9647386",

language = "Ingl{\'e}s",

series = "2021 IEEE URUCON, URUCON 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Lim, KS, Moya-Bello, E & Chavarria-Zamora, L 2021, Resource Optimization of the Eulerian Video Magnification Algorithm Towards an Embedded Architecture. in 2021 IEEE URUCON, URUCON 2021. 2021 IEEE URUCON, URUCON 2021, Institute of Electrical and Electronics Engineers Inc., pp. 576-579, 2021 IEEE URUCON, URUCON 2021, Montevideo, Uruguay, 24/11/21. https://doi.org/10.1109/URUCON53396.2021.9647386

Resource Optimization of the Eulerian Video Magnification Algorithm Towards an Embedded Architecture. / Lim, Ki Sung; Moya-Bello, Eduardo; Chavarria-Zamora, Luis.
2021 IEEE URUCON, URUCON 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 576-579 (2021 IEEE URUCON, URUCON 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Resource Optimization of the Eulerian Video Magnification Algorithm Towards an Embedded Architecture

AU - Lim, Ki Sung

AU - Moya-Bello, Eduardo

AU - Chavarria-Zamora, Luis

PY - 2021

Y1 - 2021

N2 - The use of algorithms like the Eulerian Video Magnification (EVM) could make a low-cost alternative for monitoring vital signs. A remote, non-invasive patient monitoring is advantageous, and it is possible using EVM. However, computational resources may be optimized to be executed in memory and power efficient computer architectures. Current implementations of the EVM lack of parallelization and its memory management can be improved using low-level languages. Our project seeks to optimize the Magnification algorithm to detect vital signs like respiratory and heart rate, non-invasive and more efficiently. According to our tests, both execution times and memory use are improved. The obtained results show an average improvement of 434% in execution times, with a maximum speedup of 746%. In addition, the implemented algorithm utilizes 200 MB less memory in average.

AB - The use of algorithms like the Eulerian Video Magnification (EVM) could make a low-cost alternative for monitoring vital signs. A remote, non-invasive patient monitoring is advantageous, and it is possible using EVM. However, computational resources may be optimized to be executed in memory and power efficient computer architectures. Current implementations of the EVM lack of parallelization and its memory management can be improved using low-level languages. Our project seeks to optimize the Magnification algorithm to detect vital signs like respiratory and heart rate, non-invasive and more efficiently. According to our tests, both execution times and memory use are improved. The obtained results show an average improvement of 434% in execution times, with a maximum speedup of 746%. In addition, the implemented algorithm utilizes 200 MB less memory in average.

KW - Computational resources

KW - Eulerian Video Magnification

KW - Parallelization

KW - Video processing

KW - Vital signs

UR - https://www.scopus.com/pages/publications/85124342961

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DO - 10.1109/URUCON53396.2021.9647386

M3 - Contribución a la conferencia

AN - SCOPUS:85124342961

T3 - 2021 IEEE URUCON, URUCON 2021

SP - 576

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BT - 2021 IEEE URUCON, URUCON 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE URUCON, URUCON 2021

Y2 - 24 November 2021 through 26 November 2021

ER -

Resource Optimization of the Eulerian Video Magnification Algorithm Towards an Embedded Architecture

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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