Publication detail

Evolutionary Approximation of Gradient Orientation Module in HOG-based Human Detection System

WIGLASZ, M. SEKANINA, L.

Original Title

Evolutionary Approximation of Gradient Orientation Module in HOG-based Human Detection System

Type

conference paper

Language

English

Original 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 Cartesian genetic programming (CGP) to exploit the error resilience in the HOG algorithm. We evolved new approximate implementations of the arctan function, which is typically employed to compute the gradient orientations. When the best evolved approximations are integrated into the SW implementation of the HOG algorithm, not only the execution time, but also the classification accuracy was improved in comparison with the accurate implementation and the state-of-the-art approximate implementations.

Keywords

Functional approximation, Cartesian genetic programming, Histogram of oriented gradients

Authors

WIGLASZ, M.; SEKANINA, L.

Released

14. 11. 2017

Publisher

IEEE Signal Processing Society

Location

Montreal

ISBN

978-1-5090-5989-8

Book

2017 IEEE Global Conference on Signal and Information Processing GlobalSIP 2017

Pages from

1300

Pages to

1304

Pages count

5

URL

https://www.fit.vut.cz/research/publication/11441/

BibTex

@inproceedings{BUT144438,
  author="Michal {Wiglasz} and Lukáš {Sekanina}",
  title="Evolutionary Approximation of Gradient Orientation Module in HOG-based Human Detection System",
  booktitle="2017 IEEE Global Conference on Signal and Information Processing GlobalSIP 2017",
  year="2017",
  pages="1300--1304",
  publisher="IEEE Signal Processing Society",
  address="Montreal",
  doi="10.1109/GlobalSIP.2017.8309171",
  isbn="978-1-5090-5989-8",
  url="https://www.fit.vut.cz/research/publication/11441/"
}