计算机工程 ›› 2009, Vol. 35 ›› Issue (14): 241-242.doi: 10.3969/j.issn.1000-3428.2009.14.084

• 人工智能及识别技术 • 上一篇    下一篇

基于Boosting的智能车辆多类障碍物识别

沈志熙,黄席樾,杨镇宇,韦金明   

  1. (重庆大学自动化学院,重庆 400030)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-07-20 发布日期:2009-07-20

Boosting-based Multi-class Obstacles Recognition of Intelligent Vehicle

SHEN Zhi-xi, HUANG Xi-yue, YANG Zhen-yu, WEI Jin-ming   

  1. (College of Automation, Chongqing University, Chongqing 400030)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-07-20 Published:2009-07-20

摘要: 提出一种基于Boosting集成学习的二叉树支持向量机(BBT-SVM)。根据城区交通环境中各类障碍物的出现概率、模式间的类间差异,设计适用于智能车辆障碍物识别的SVM树型结构。对每个节点SVM分类器采用Boosting集成学习方法进行改进,减少差错积累误差,提高分类精度和泛化能力。实验结果表明,该方法能有效地对城区交通场景中6类常规障碍物模式进行实时在线识别。

关键词: 智能车辆, 障碍物识别, 支持向量机, 集成学习, Boosting算法

Abstract: A novel Boosting-based Binary Tree-SVM(BBT-SVM) is presented. Based on the distributing probability and pattern diversity of each obstacle in urban traffic scenes, a compatible tree structure of SVM is designed. A Boosting-based ensemble learning approach is applied to reduce the transfer error and it improves the generalization performance of per-node classifier. The improved BBT-SVM can correctly recognize six kinds of normal obstacle patterns in urban traffic scenes. Experimental results show the improved BBT-SVM can efficiently recognize six kinds of normal obstacle patterns in urban traffic scenes.

Key words: intelligent vehicle, obstacle recognition, Support Vector Machine(SVM), ensemble learning, Boosting algorithm

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