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Proceedings Paper

HOG and SVM algorithm based on vehicle model recognition
Author(s): Jinkun Yang; Zhong Chen; Jiahao Zhang; Changheng Zhang; Qianqian Zhou; Jian Yang
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Paper Abstract

With the rapid development of machine learning, computer vision and other artificial intelligence technologies, vehicle identification based on image processing, pattern recognition and other technologies has attracted more and more attention and research. As an important part of intelligent transportation system, vehicle type identification plays an important role in traffic management, campus entrance guard and other scenarios. This paper proposes a HOG feature based vehicle model recognition algorithm for the recognition of road passing vehicles. First, HOG feature vector of vehicle samples is extracted through HOG algorithm. Then, SVM classifier is used to train the HOG feature vector of training samples. The HOG feature vector of test samples is put into SVM classifier to obtain the classification result of test samples. In this paper, according to the wheelbase and displacement classification, the vehicle types are divided into: micro car, small car, compact car, medium car, medium and large car, luxury car, MPV, SUV, minivan nine types and establish training samples and test sample model library, the overall recognition success rate is 93.6893%.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300T (14 February 2020); doi: 10.1117/12.2538191
Show Author Affiliations
Jinkun Yang, Huazhong Univ. of Science and Technology (China)
Zhong Chen, Huazhong Univ. of Science and Technology (China)
Jiahao Zhang, Huazhong Univ. of Science and Technology (China)
Changheng Zhang, Huazhong Univ. of Science and Technology (China)
Qianqian Zhou, Huazhong Univ. of Science and Technology (China)
Jian Yang, Institute of Aerospace Information Innovation (China)

Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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