Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)

Vehicle Identification Using Wavelet Entropy and Particle Swarm Optimization Support Vector Machine

Authors
Fangzhou Bao, Koji Nakamura
Corresponding Author
Fangzhou Bao
Available Online May 2018.
DOI
https://doi.org/10.2991/amcce-18.2018.119How to use a DOI?
Keywords
Wavelet Entropy, Particle Swarm Optimization, Support Vector Machine, Vehicle Identification
Abstract
In order to identify Ford vehicles from non-Ford vehicles, this paper proposed a novel method based on the combination of wavelet entropy, particle swarm optimization, and support vector machine. We collect a 100-image dataset, 50 are Ford vehicles and the rest 50 are non-Ford vehicles. The results show that our method obtained a sensitivity of 82.20± 3.94%, a specificity of 81.60± 3.50%, and an accuracy of 81.90± 0.74%. In all, this method is promising in vehicle identification.
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Proceedings
2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
Part of series
Advances in Engineering Research
Publication Date
May 2018
ISBN
978-94-6252-508-5
ISSN
2352-5401
DOI
https://doi.org/10.2991/amcce-18.2018.119How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Fangzhou Bao
AU  - Koji Nakamura
PY  - 2018/05
DA  - 2018/05
TI  - Vehicle Identification Using Wavelet Entropy and Particle Swarm Optimization Support Vector Machine
BT  - 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.119
DO  - https://doi.org/10.2991/amcce-18.2018.119
ID  - Bao2018/05
ER  -