AN IMPROVED NON-PARAMETRIC BACKGROUND MODEL AND TWO-LEVEL CLASSIFIER FOR TRAFFIC INFORMATION RECOGNITION

摘要：

Acquirement of real-time and overall traffic information is very important for improving road network efficiency and reducing traffic congestion. This paper proposed an improved non-parametric background model to segment the moving vehicles from traffic videos with limited computational complexity and space complexity. With the analysis of characteristics of traffic parameters, a two-level classifier is proposed for automatic recognition of traffic information. The results from automatic recognition have high coincidence rate with those from expert classification.

关键词： traffic engineering non-parametric background model two-layer classifier traffic information recognition

作者: Song Bi Liqun Han Yixin Zhong Xiaojie Wang Hairu Guo

作者单位: Center for Intelligence Science and Technology Beijing University of Posts and Telecommunications, Beijing, China

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Cloud Computing and Intelligence Systems(2011年第一届IEEE云计算与智能系统国际会议 IEEE CCIS2011)

会议地点: 北京

会议语种:英文

页码: 495-499

在线出版日期: 2011-09-15（万方平台首次上网日期，不代表论文的发表时间）