Vehicle Logo Recognition Based on Sparse Sampling and Gradient Distribution Features

doi:10.16182/j.issn1004731x.joss.201709021

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.

Key words: image processing, gray-scale distribution, gradient distribution, multi-scale detection, vehicle logo location, vehicle-logo recognition

CLC Number:

TP391.4

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.

References

[1] 黄敏, 钮中铭, 张小兰, 等. 基于GIS-T的交通仿真路网构建研究[J]. 系统仿真学报, 2017, 29(3): 531-536.
(Huang Min, Niu Zhongming, Zhang Xiaolan, et al.Research of the Construction of GIS-T Traffic Simulation Road Network[J]. Journal of System Simulation (S1004-731X), 2017, 29(3): 531-536.)
[2] 魏平顺. 智能交通系统中车标图像识别技术研究 [D]. 南京: 南京理工大学, 2013.
(Wei Pingshun.Research on the recognition technology of vehicle logos in intelligent transportation system [D]. Nanjing, China: Nanjing University of Technology, 2013.)
[3] 李侠, 李文举, 孙娟红, 等. 基于模板匹配和边缘检测的车标定位方法[J]. 计算机工程与设计, 2011, 32(10): 3453-3456.
(Li Xia, Li Wenju, Sun Juanhong, et al.Approach for vehicle-logo location based on template matching and edge detection[J]. Computer Engineering and Design, 2011, 32(10): 3453-3456.)
[4] 周阳. 基于图像的车标定位与识别 [D]. 北京: 北方工业大学, 2015.
(Zhou Yang.Image based vehicle logo location and recognition [D]. Beijing, China: North China University of Technology, 2015.)
[5] 李熙莹, 吕硕, 袁敏贤. 基于散热器栅格背景精确分类的车标定位方法[J]. 计算机工程与应用, 2017, 53(2): 206-213.
(Li Xiying, Lv Shuo, Yuan Minxian.Vehicle logo location method based on accurate radiator grid back-ground classification[J]. Computer Engineering and Applications, 2017, 53(2): 206-213.)
[6] 朱资淘. 基于SIFT算子的车标识别软件 [D]. 广州: 华南理工大学, 2013.
(Zhu Zitao.Vehicle Logo recognition software based on SIFT operator [D]. Guangzhou, China: South China University of Technology, 2013.)
[7] 陈舜杰. 快速车标识别算法的研究 [D]. 上海: 上海交通大学, 2013.
(Chen Shunjie.Research on the rapid recognition algorithm of the vehicle logo [D]. Shanghai, China: Shanghai Jiaotong University, 2013.)
[8] 王建. 基于图像的智能交通系统中车标识别技术研究 [D]. 南京: 南京理工大学, 2014.
(Wang Jian.The research of vehicle logo recognition technology in intelligent transportation system [D]. Nanjing, China: Nanjing University of Technology, 2014.)
[9] 王永杰, 裴明涛, 贾云得. 多信息融合的快速车牌定位[J]. 中国图象图形学报, 2014, 19(3): 471-475.
(Wang Yongjie, Pei Mingtao, Jia Yunde.License plate detection based on multiple features[J]. Journal of Image and Graphics, 2014, 19(3): 471-475.)
[10] 周爱珍. 基于稀疏采样的医学成像方法研究 [D]. 广州: 南方医科大学, 2011.
(Zhou Aizhen.Research on medical imaging method based on sparse sampling [D]. Guangzhou, China: Southern Medical University, 2011.)
[11] Hongyin Shi, Baojing Jia.SAR imaging method based on coprime sampling and nested sparse sampling[J]. Journal of Systems Engineering and Electronics (S1004-4132), 2015, 6: 1222-1228.