JISE

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Journal of Information Science and Engineering, Vol. 26 No. 3, pp. 769-783

A Distributed Threshold Algorithm for Vehicle Classification Based on Binary Proximity Sensors and Intelligent Neuron Classifier

WEI ZHANG, GUO-ZHEN TAN, HUI-MIN SHI AND MING-WEN LIN
Department of Computer Science and Engineering
Dalian University of Technology
Dalian, Liaoning, 116023 P.R. China
E-mail: gztan@dlut.edu.cn

To improve the accuracy of real time vehicle surveillance, utilize the advances in wireless sensor networks to develop a magnetic signature and length estimation based vehicle classification methodology with binary proximity magnetic sensor networks and intelligent neuron classifier. In this algorithm, we use the low cost and high sensitive magnetic sensors to measure the magnetic field distortion when vehicle crosses the sensors and detect vehicle via an adaptive threshold. The vehicle length is estimated with the geometrical characteristics of the proximity sensor networks, and finally identifies vehicle type from an intelligent neural network classifier. Simulation and on-road experiment obtains high recognition rate over 90%. It verified that this algorithm enhances the vehicle surveillance with high accuracy and solid robustness.

Keywords: real-time traffic surveillance, vehicle detection, vehicle classification, wireless sensor networks, binary proximity sensor networks, intelligent neurons, distributed threshold, adaptive, clustering

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