基于Boosting的智能车辆多类障碍物识别

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。

Boosting-based Multi-class Obstacles Recognition of Intelligent Vehicle

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...