Unsupervised Vehicle-Classifier Learning Method using It-means and Support Vector Machine

摘要：

Vehicle classification is an essential function of vehicle detectors that provide real-time traffic information to traffic control system. This investigation proposed an unsupervised vehi-cle classification method which combined k-means clustering algorithm support vector machine. This method deals with features derived from FMCW radar signal and able to classify two vehicle classes. The numerical example shows a significant result of less than 2% classification error.

关键词： Signal Processing SVM pattern recognition vehicle detection FMCW radar

作者: Chien-Lun Lan Rih-Jin Li Chia-Chun Hsu

作者单位: Department of Transportation Technology and Management National Chiao Tung University,1001 Ta Hsueh Road,Hsinchu, Taiwan

会议类型: 国际会议

会议名称: The First World Congress on Global Optimization in Engineering & Science(第一届工程与科学全局优化国际会议 WCGO2009)

会议地点: 长沙

会议语种:英文

页码: 650-656

在线出版日期: 2009-06-01（万方平台首次上网日期，不代表论文的发表时间）