Boosting-based Multi-class Obstacles Recognition of Intelligent Vehicle
Wei Jin-ming · Jisuanji gongcheng · 2009
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.