Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering

An Optimization Algorithm for Pedestrian Detection ahead of Vehicle

Authors
A.Y. Guo, M.H. Xu, F. Ran, C.Q. Du
Corresponding Author
A.Y. Guo
Available Online July 2015.
DOI
10.2991/aiie-15.2015.76How to use a DOI?
Keywords
pedestrian detection HOG LBP ROI (region of interest)
Abstract

The pedestrian detection ahead of vehicle is a very important technology in automobile driving system. Histogram of Oriented Gradients (HOG) plus Support Vector Machine (SVM) is the general algorithm in the pedestrian detection. But, most of algorithms are obtained only by the Matlab or Open CV, rarely focused on the transportation to the hardware and self-develope C language library. Therefore, this paper proposed a new algorithm of cascaded Local Binary Pattern (LBP) and HOG detecting pedestrian based on the linear SVM. This algorithm can be transported to the ARM, DSP and some other embedded systems. The experiment shows the result very well.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
Series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-62520-70-7
ISSN
1951-6851
DOI
10.2991/aiie-15.2015.76How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - A.Y. Guo
AU  - M.H. Xu
AU  - F. Ran
AU  - C.Q. Du
PY  - 2015/07
DA  - 2015/07
TI  - An Optimization Algorithm for Pedestrian Detection ahead of Vehicle
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 272
EP  - 275
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.76
DO  - 10.2991/aiie-15.2015.76
ID  - Guo2015/07
ER  -