Multi class obstacles recognition for intelligent vehicle in urban traffic scenes

For multi class obstacles recognition for intelligent vehicle in urban traffic scenes, an improved Binary Tree Support Vector Machine (BT SVM) based on ensemble learning is presented. Based on the distributing probability and pattern diversity of each obstacle in urban traffic scenes, a compatible tree structure of BT SVM is designed. An approach based on AdaBoost ensemble learning is applied to reduce the transfer error and improve the accuracy and generalization ability of per node classifier. The proposed method can efficiently recognize 6 kinds of normal obstacle patterns in urban traffic scenes.