基于Haar-like特征与Adaboost算法的前方车辆辨识技术研究
DOI:
作者:
作者单位:

1. 华东交通大学南昌330013; 2. 长安大学汽车学院西安710064

作者简介:

通讯作者:

中图分类号:

TN081

基金项目:

国家自然科学基金(51278062)资助项目


Research of preceding vehicle identification based on Haar-like features and Adaboost algorithm
Author:
Affiliation:

1. East China Jiaotong University, Nanchang 330013,China; 2. School of Automobile, Chang’an University, Xi’an 710064,China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    为了提高前方车辆的辨识效能,提出一种融合Haarlike特征与Adaboost算法的前方车辆辨识方法,基于海量车辆样本集进行离线训练,提取有效车辆轮廓与纹理特征,以Haarlike特征作为目标描述方法,采用Adaboost机器学习算法训练分类器,并构建特征样本级联分类器,对测试对象进行车辆存在性检测。试验结果表明,提出的融合Haarlike与Adaboost的车辆辨识算法检测准确率为91%以上,平均检测速率28 ms,对车辆类型和环境干扰等非确定因素具有较强的自适应能力,提高了前方车辆纵向检测的鲁棒性,满足了车辆纵向维度的安全行驶应用需求。

    Abstract:

    In order to improve the efficiency of the preceding vehicle identification, a preceding vehicle identification algorithm combined Haarlike features and Adaboost algorithm was proposed. Based on massive amounts of offline training sample set, effective vehicle contour and texture characteristics was extracted, Haarlike characteristics was used to describe the goal. Adaboost machine learning algorithms was used to trained classifier, the sample characteristics of cascade classifier was built, and the test object was used to detect the vehicle existence. The experimental result shows that the algorithm based on Haarlike features and Adaboost algorithm in the paper has a high detection rate of above 91%, and average detection speed of 28 ms, it can well adapt to the uncertain factors such as environmental interference and vehicle type, improve the robustness of the preceding vehicle detection, also meet the requirement of the safe driving in the longitudinal dimensionality.

    参考文献
    相似文献
    引证文献
引用本文

朱志明,乔洁.基于Haar-like特征与Adaboost算法的前方车辆辨识技术研究[J].电子测量技术,2017,40(5):180-184

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2017-07-13
  • 出版日期: