Vehicle Identification Using Wavelet Entropy and Particle Swarm Optimization Support Vector Machine
Fangzhou Bao, Koji Nakamura
Available Online May 2018.
- 10.2991/amcce-18.2018.119How to use a DOI?
- Wavelet Entropy, Particle Swarm Optimization, Support Vector Machine, Vehicle Identification
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.
- © 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/).
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 - 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 -