Abstract
Luo Xianglong Wu Qisheng Niu Guohong School of Information Engineering, Chang’an UniversityS PH(86)029-82334956; FAX(86)029-82334357; email: xlluo@chd.edu.cn. School of ElectronicC PH(86)029-82334956; FAX(86)02982334357; email:qshwu@chd.edu.cn. Xi’an Municipal Engineering DesignR PH(86)029-88421567; email:newgirl8074@yahoo.com. ABSTRACT Traffic incident automatic detection and recognition play an important role in traffic management and controlling. For the problems of automatic traffic incident detection and recognition, a method is proposed based on wavelet decomposition and support vector machine (SVM) with vehicle acoustic signals. Vehicle acoustic signals are decomposed with the wavelet analysis, and the power in different frequency is regarded as the different incident eigenvectors and also as training samples of SVM traffic incident classifier. By the processing of normal driving, braking and crash incident acoustic signals, the result shows that the proposed method is an effective method for automatic traffic incident detection and recognition.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
