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| 基于改进HOG特征值的车标检测与识别方法 | |
| 引用本文： | 鲁丰,刘芸,张仁辉.基于改进HOG特征值的车标检测与识别方法[J].光通信研究,2012,38(5):26-29. |
| 作者姓名： | 鲁丰 刘芸 张仁辉 |
| 作者单位： | 1. 清华大学电子工程系,北京,100084 2. 北京大学计算机科学与技术系,北京,100871 3. 武汉烽火众智数字技术有限责任公司,湖北武汉,430074 |
| 摘 要： | 车标自动识别是智能交通系统中机动车辆信息采集的关键内容。根据车标具有丰富边缘信息的特征,文章应用HOG(梯度方向直方图)的特征值,采用SVM(支持向量机)的分类工具实现了车标的快速检测与识别。并提出一种改进的HOG特征值,在车标检测识别准确率上取得了显著的效果。大量实验数据以及在智能交通系统中的应用表明,该方法具有较强的鲁棒性和实用价值。 |
| 关 键 词： | 梯度方向直方图 支持向量机 车标识别 车标定位 |
| 收稿时间： | 2012/7/23 |
An improved HOG-based vehicle logo location and recognition method |
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| Lu Feng , Liu Yun , Zhang Renhui.An improved HOG-based vehicle logo location and recognition method[J].Study on Optical Communications,2012,38(5):26-29. | |
| Authors: | Lu Feng Liu Yun Zhang Renhui |
| Affiliation: | 1.Department of Electronic Engineering,Tsinghua University,Beijing 100084,China; 2.Department of Computer Science and Technology,Peking University,Beijing 100871,China; 3.Wuhan FiberHome Digital Technology Co.,Ltd.,Wuhan 430074,China) |
| Abstract: | Automatic logo recognition is one of the key elements of vehicle information collection in an intelligent transportation system (ITS). Considering the abundant marginal information about the vehicle logo, this paper applies the characteristic values of the Histograms of Oriented Gradients (HOG) and implements the fast vehicle logo detection and recognition with the help of Support Vector Machine (SVM). Furthermore, it proposes an improved HOG, which achieves evident results in terms of the accuracy of vehicle logo detection and recognition. Large amounts of experimental data and applications in ITS show that this method is fairly robust and practically useful. |
| Keywords: | HOG SVM vehicle logo recognition vehicle logo location |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |