Cooperative Coevolutionary Approximation in HOG-based Human Detection Embedded System
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Wiglasz:2018:ieeeCompIntl,
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author = "Michal Wiglasz and Lukas Sekanina",
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booktitle = "2018 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "Cooperative Coevolutionary Approximation in
{HOG-based} Human Detection Embedded System",
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year = "2018",
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pages = "1313--1320",
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month = "18-21 " # nov,
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address = "Bangalore, India",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming, Approximate computing, Cooperative
coevolution",
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DOI = "doi:10.1109/SSCI.2018.8628910",
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size = "8 pages",
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abstract = "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.",
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notes = "Replaces \cite{Wiglasz:2017:GlobalSIP}
SVM LIBLINEAR
p1319 'CGP to evolve new approximate implementations of
the arctan and square root functions'
Also known as \cite{8628910}",
- }
Genetic Programming entries for
Michal Wiglasz
Lukas Sekanina
Citations