Multi-traffic objects classification using support vector machine

摘要：

In order to classify the traffic objects in multi-traffic scenes, six classes were divided firstly, then eight features base on shape and motion information are extracted. The eight features of traffic objects will be the input of the support vector machine (SVM) classifier which is contrasted with RBF neural network classifier. The object type is classified according to the output of the SVM. Experimental results based on actually scene video indicate that the algorithm could classify the traffic objects in multi-traffic scenes at a high recognition ratio.

关键词： Multi traffic Classification Support Vector Machine (SVM)

作者: Neng Sheng Hui Wang Hong Liu

作者单位: Dept. of Control Science & Engineering, Zhejiang University, Hangzhou 310027

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 3215-3218

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）