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Proceedings Paper

Time-delay neural network for audio monitoring of road traffic and vehicle classification
Author(s): Amir Y. Nooralahiyan; Louis Lopez; Denis Mckewon; Masoud Ahmadi
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Paper Abstract

The aim of this research is to investigate the feasibility of developing a cost effective traffic monitoring detector for the purpose of reliable on-line vehicle classification to aid traffic management systems. The detector used was a directional microphone connected to a DAT recorder. The digital signal was preprocessed by LPC (Linear Predictive Coding) parameter conversion based on autocorrelation analysis. A Time Delay Neural Network (TDNN) was chosen to classify individual travelling vehicles based on their speed-independent acoustic signature. The network was trained and tested with real data for four types of vehicles. The paper provides a description of the TDNN architecture and training algorithm and an overview of the LPC pre-processing and feature extraction technique as applied to audio monitoring of road traffic. The performance of TDNN vehicle classification, convergence and accuracy for the training patterns are fully illustrated. In generalizing to a limited number of test patterns available, 100% accuracy in classification was achieved. The net was also robust to changes in the starting position of the acoustic waveforms with 86% accuracy for the same test data set.

Paper Details

Date Published: 17 February 1997
PDF: 8 pages
Proc. SPIE 2902, Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS, (17 February 1997); doi: 10.1117/12.267145
Show Author Affiliations
Amir Y. Nooralahiyan, Univ. of Leeds (United Kingdom)
Louis Lopez, Univ. of Leeds (United Kingdom)
Denis Mckewon, Univ. of Leeds (United Kingdom)
Masoud Ahmadi, Univ. of Leeds (United Kingdom)

Published in SPIE Proceedings Vol. 2902:
Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS
Alan C. Chachich; Marten J. de Vries, Editor(s)

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