Vehicle Classification under Different Feature Sets with a Single Anisotropic Magnetoresistive Sensor

Chang XU  Yingguan WANG  Yunlong ZHAN  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A   No.2   pp.440-447
Publication Date: 2017/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E100.A.440
Type of Manuscript: Special Section PAPER (Special Section on Intelligent Transport Systems)
anisotropic magnetoresistive sensor,  vehicle classification,  intelligent transport system,  

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This paper focus on the development of a single portable roadside magnetic sensor for vehicle classification. The magnetic sensor is a kind of anisotropic magnetic device that do not require to be embedded in the roadway-the device is placed next to the roadway and measure traffic in the immediately adjacent lane. A novel feature extraction and comparison approach is presented for vehicle classification with a single magnetic sensor, which is based on four different feature sets extracted from the detected magnetic signal. Furthermore, vehicle classification has been achieved with three common classification algorithms, including support vector machine, k-nearest neighbors and back-propagation neural network. Experimental results have demonstrated that the Peak-Peak feature set with back-propagation neural network approach performs much better than other approaches. Besides, the normalization technology has been proved it does work.