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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2010  >  10(2): 116-121
LUO Xiang-long, GAO Jing-huai, NIU Guo-hong, PAN Ruo-yu. Traffic incident acoustic recognition method based on wavelet decomposition and support vector machine[J]. Journal of Traffic and Transportation Engineering, 2010, 10(2): 116-121. doi: 10.19818/j.cnki.1671-1637.2010.02.021
Citation: LUO Xiang-long, GAO Jing-huai, NIU Guo-hong, PAN Ruo-yu. Traffic incident acoustic recognition method based on wavelet decomposition and support vector machine[J]. Journal of Traffic and Transportation Engineering, 2010, 10(2): 116-121. doi: 10.19818/j.cnki.1671-1637.2010.02.021
shu

Traffic incident acoustic recognition method based on wavelet decomposition and support vector machine

doi: 10.19818/j.cnki.1671-1637.2010.02.021
  • 1.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China

  • 3.

    Xi'an Municipal Engineering Design and Research Institute Co., Ltd., Xi'an 710068, Shaanxi, China

  • 4.

    School of Communication and information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710064, Shaanxi, China

More Information
  • Author Bio:

    LUO Xiang-long(1978-), male, lecturer, doctoral student, +86-29-82334956, xlluo@chd.edu.cn

    GAO Jing-huai(1960-), male, professor, PhD, +86-29-82665060, +86-29-82665060, jhgao@mail.xjtu.edu.cn

  • Received Date: 2009-12-12
  • Publish Date: 2010-04-25
    Abstract
  • The existing automatic detection and recognition methods of traffic incidents were analyzed, a recognition method with vehicle acoustic signals was proposed based on wavelet decomposition(WD) and support vector machine(SVM). Vehicle acoustic signals were decomposed with WD, the powers in different frequencies were regarded as different incident eigenvectors, and the traffic incident classifier composed of multiple SVMs was trained. The acoustic signals of normal driving, braking and crash incidents were recognized. Test result shows that various traffic incidents can be recognized with vehicle acoustic signals, the recognition rate reaches 95%, so the proposed method is feasible.

     

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    • References(16)
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