Vehicle Logo Recognition Based on Sparse Sampling and Gradient Distribution Features

Authors

Binbin Zhou, 1. College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223001, China;;2. Virtual Reality and Human-Computer Interaction Research Center, Hangzhou Normal University, Hangzhou 311121, China;
Shangbing Gao, 1. College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223001, China;;
Zhigeng Pan, 1. College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223001, China;;2. Virtual Reality and Human-Computer Interaction Research Center, Hangzhou Normal University, Hangzhou 311121, China;
Liangliang Wang, 1. College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223001, China;;
Hongyang Wang, 1. College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223001, China;;

Abstract

Abstract: The vehicle logo location and recognition are separated in the traditional method, the location errors will affect the subsequent recognition, at the same time the vehicle logo images are with low resolution and poor quality. Thus, a novel method was proposed which integrated the vehicle logo location and recognition organically. The sample images were sampled by sparse sampling, and then the point set was divided into adjacent point set and non adjacent point set, and the gradient feature and light and dark feature were extracted respectively, constructing the feature library. The logo coarse location area was multi-scale scanned. The experimental results show that the proposed method is superior to other advanced algorithms on the vehicle detection and recognition efficiency, and robust to the different types of logo images.

Recommended Citation

Zhou, Binbin; Gao, Shangbing; Pan, Zhigeng; Wang, Liangliang; and Wang, Hongyang (2020) "Vehicle Logo Recognition Based on Sparse Sampling and Gradient Distribution Features," Journal of System Simulation: Vol. 29: Iss. 9, Article 21.
DOI: 10.16182/j.issn1004731x.joss.201709021
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss9/21

First Page

2035

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201709021

Last Page

2042

CLC

TP391.4

Recommended Citation

Zhou Binbin, Gao Shangbing, Pan Zhigeng, Wang Liangliang, Wang Hongyang. Vehicle Logo Recognition Based on Sparse Sampling and Gradient Distribution Features[J]. Journal of System Simulation, 2017, 29(9): 2035-2042.

Corresponding Author

Shangbing Gao,

DOI

10.16182/j.issn1004731x.joss.201709021