论文信息 - An improved non-parametric background model and two-level classifier for traffic information recognition

An improved non-parametric background model and two-level classifier for traffic information recognition

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.

Yixin Zhong | Xiaojie Wang | Song Bi | Hairu Guo | Liqun Han

[1] Azeddine Beghdadi,et al. On the analysis of background subtraction techniques using Gaussian Mixture Models , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[2] Xuehua Song,et al. Moving object detection based on an improved gaussian mixture background model , 2009, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management.

[3] L. Davis,et al. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.

[4] B. Ripley,et al. Pattern Recognition , 1968, Nature.

[5] Rong Jian,et al. Commentary of Integration of Traffic Control and Traffic Flow Guidance , 2008 .

[6] He Guo-guang. Description and simulation of dynamic route guidance system road network , 2009 .

[7] Banavar Sridhar,et al. Modeling and Optimization in Traffic Flow Management , 2008, Proceedings of the IEEE.