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

DOI

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.

Copyright

© 2018, 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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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  - Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SP  - 689
EP  - 695
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.119
DO  - 10.2991/amcce-18.2018.119
ID  - Bao2018/05
ER  -