Vision-based Vehicle Tracking and Classification on the Highway

作     者：Guolian Yun Qimei Chen Bo Li Xindao Wang 

作者机构：Department of Electronic Science and Engineering Nanjing University Nanjing 210093 China 

出 版 物：《Journal of Systems Science and Information》 (系统科学与信息学报（英文）)

年 卷 期：2007年第5卷第2期

页      面：141-149页

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

主　　题：vision-based vehicle tracking Bayesian network vehicle classification 

摘      要：This paper presents algorithms for vision-based tracking and classification of vehicles in image sequences of traffic scenes recorded by a stationary camera. In the algorithms, the central moment and extended Kalman filter of tracking processes optimizes the amount of spent computational resources. Moreover, it robust to many difficult situations such as partial or full occlusions of vehicles. Vehicle classification performance is improved by Bayesian network, especially from incomplete data. The methods are test on a single Intel Pentium 4 processor 2.4 GHz and the frame rate is 25 frames/s. Experimental results from highway scenes are provided, which demonstrate the effectiveness and robust of the methods.