Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Vehicle Type Classification based on Improved HOG_SVM

Authors
Penghua Ge, Yanping Hu
Corresponding Author
Penghua Ge
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.102How to use a DOI?
Keywords
Vehicle type classification; HOG_SVM; feature extraction; PCA.
Abstract
There are few differences in the characteristics of vehicles and many interference factors in vehicle identification, especially in complex backgrounds. In order to improve the accuracy of image feature extraction and recognition in complex background, a vehicle-types recognition technology based on improved HOG_SVM is proposed in this paper. In order to obtain abundant vehicle identification information, we perform targeted image preprocessing methods such as grayscale stretching and Gaussian filtering on the original image to reduce background interference factors. The HOG feature is then introduced to obtain rich features of the image, and the SVM classifier in machine learning is trained at the output layer by multitasking learning of a large amount of tagged data. Different from the traditional method, the PCA dimension reduction process is used to speed up the recognition of the improved HOG feature, and the method of SVM is used to avoid the classifier from falling into the local minimum. In this paper, the public vehicle dataset is used as the classifier training dataset and test dataset, and the proposed method is verified by experiments.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.102How 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  - Penghua Ge
AU  - Yanping Hu
PY  - 2019/04
DA  - 2019/04
TI  - Vehicle Type Classification based on Improved HOG_SVM
PB  - Atlantis Press
SP  - 640
EP  - 647
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.102
DO  - https://doi.org/10.2991/icmeit-19.2019.102
ID  - Ge2019/04
ER  -