Chapter
Apr 26, 2012
Vehicle Classification Using Acoustic Energy Signature in Wavelet Scale Space and Neural Network
Authors: Jinghua Li [email protected], Jiadong Xu [email protected], and Hongjuan Li [email protected]Author Affiliations
Publication: International Conference on Transportation Engineering 2007
Abstract
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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© 2007 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Acoustics
- Algorithms
- Artificial intelligence and machine learning
- Automobiles
- Computer programming
- Computing in civil engineering
- Detection methods
- Engineering fundamentals
- Highway transportation
- Infrastructure
- Mathematical functions
- Mathematics
- Methodology (by type)
- Neural networks
- Traffic engineering
- Traffic management
- Traffic signals
- Transportation engineering
- Vector analysis
- Vehicles
- Wavelets
Authors
Affiliations
School of Electronics and Information, Northwestern Polytechnical University, No.127 West Youyi Road, Xi'an City, Shanxi Province (710072);. E-mail: [email protected]
School of Electronics and Information, Northwestern Polytechnical University, No.127 West Youyi Road, Xi'an City, Shanxi Province (710072). E-mail: [email protected]
School of Aeronautics, Northwestern Polytechnical University, No.127 West Youyi Road, Xi'an City, Shanxi Province (710072). E-mail: [email protected]
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