Cooperative Coevolutionary Approximation in HOG-based Human Detection Embedded System

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU130715" target="_blank" >RIV/00216305:26230/18:PU130715 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.fit.vut.cz/research/publication/11695/" target="_blank" >https://www.fit.vut.cz/research/publication/11695/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SSCI.2018.8628910" target="_blank" >10.1109/SSCI.2018.8628910</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cooperative Coevolutionary Approximation in HOG-based Human Detection Embedded System

Popis výsledku v původním jazyce

The histogram of oriented gradients (HOG) feature extraction is a computer vision method widely used in embedded systems for detection of objects such as pedestrians. We used cooperative coevolutionary Cartesian genetic programming (CGP) to exploit the error resilience in the HOG algorithm. We evolved new approximate implementations of the arctan and square root functions, which are typically employed to compute the gradient orientations and magnitudes. When the best evolved approximations are integrated into the software implementation of the HOG algorithm, not only the execution time, but also the classification accuracy was improved in comparison with approximations evolved separately using CGP and also compared to the state-of-the art approximate implementations. As the evolved code does not contain any loops and branches, it is suitable for the follow-up low-power hardware implementation.

Název v anglickém jazyce

Cooperative Coevolutionary Approximation in HOG-based Human Detection Embedded System

Popis výsledku anglicky

The histogram of oriented gradients (HOG) feature extraction is a computer vision method widely used in embedded systems for detection of objects such as pedestrians. We used cooperative coevolutionary Cartesian genetic programming (CGP) to exploit the error resilience in the HOG algorithm. We evolved new approximate implementations of the arctan and square root functions, which are typically employed to compute the gradient orientations and magnitudes. When the best evolved approximations are integrated into the software implementation of the HOG algorithm, not only the execution time, but also the classification accuracy was improved in comparison with approximations evolved separately using CGP and also compared to the state-of-the art approximate implementations. As the evolved code does not contain any loops and branches, it is suitable for the follow-up low-power hardware implementation.

Druh

D - Stať ve sborníku

CEP obor

—

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

Název statě ve sborníku

2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018)

ISBN

978-1-5386-9276-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1313-1320

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Bengaluru

Místo konání akce

Bengaluru

Datum konání akce

18. 11. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000459238800180