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

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5 Author(s)
Song Bi ; Center for Intelligence Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China ; Liqun Han ; Yixin Zhong ; Xiaojie Wang
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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.

Published in:

2011 IEEE International Conference on Cloud Computing and Intelligence Systems

Date of Conference:

15-17 Sept. 2011