Three kinds of vehicle acoustic signals: cars, jeeps, and trucks are studied. According to the analysis of vehicles acoustic signal signature in time-domain and frequency-domain, a feature extraction algorithm is proposed which take the time-domain energy of the acoustic signals in different scales after wavelet decomposition as the feature vectors. An improved BP neural network classifier for vehicle target classification is designed. Experiment results have shown that the proposed feature extraction algorithm can distinguish different types of vehicles with satisfactory rate of correct recognition, and feature vector is robust. The classification accuracies can reach as high as 92%.


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    Title :

    Vehicle Classification Using Acoustic Energy Signature in Wavelet Scale Space and Neural Network


    Contributors:
    Li, Jinghua (author) / Xu, Jiadong (author) / Li, Hongjuan (author)

    Conference:

    First International Conference on Transportation Engineering ; 2007 ; Southwest Jiaotong University, Chengdu, China



    Publication date :

    2007-07-09




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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