Road traffic automatic incident detection system is the important composition of road traffic monitoring system. On the basis of support vector machines (SVM) theory and sequential minimal optimization (SMO) algorithm, the road traffic incident detection model based on SMO was put forward. With Visual C++ and Matlab, a simulation experiment was applied to the model. The shortcomings of quadratic programming (QP) algorithm were analyzed. The effects of three different kernel functions on sample training SVM classifier and on detection performance were compared using Chunking algorithm and SMO algorithm. The results show that Gauss kernel function is better than other two kernel functions for the performance of training and incident detection using SMO.


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    Title :

    Road Traffic Incident Detection Model Based on SMO


    Contributors:
    Cong, Haozhe (author) / Fang, Shouen (author) / Guo, Jing (author)

    Conference:

    Second International Conference on Transportation Engineering ; 2009 ; Southwest Jiaotong University, Chengdu, China



    Publication date :

    2009-07-29




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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