Vehicle detection and classification by measuring and processing magnetic signal

Lan, Jinhui, Xiang, Yong, Wang, Liping and Shi, Yuqiao 2011, Vehicle detection and classification by measuring and processing magnetic signal, Measurement, vol. 44, no. 1, pp. 174-180.

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Title Vehicle detection and classification by measuring and processing magnetic signal
Author(s) Lan, Jinhui
Xiang, Yong
Wang, Liping
Shi, Yuqiao
Journal name Measurement
Volume number 44
Issue number 1
Start page 174
End page 180
Total pages 7
Publisher Elsevier BV
Place of publication Amsterdam, The Netherlands
Publication date 2011-01
ISSN 0263-2241
1873-412X
Keyword(s) MEMS magnetic sensor
vehicle detection
vehicle classification
improved SVM
Summary This paper presents novel vehicle detection and classification method by measuring and processing magnetic signal based on single micro-electro- mechanical system (MEMS) magnetic sensor. When a vehicle moves over the ground, it generates a succession of impacts on the earth's magnetic field, which can be detected by single magnetic sensor. The magnetic signal measured by the magnetic sensor is related to the moving direction and the type of the vehicle. Generally, the recognition rate using single sensor detector is not high. In order to improve the recognition rate, a novel feature extraction algorithm and a novel vehicle classification and recognition algorithm are presented. The concavity and convexity areas, and the angles of concave and convex parts of the waveform are extracted. An improved support vector machine (ISVM) classifier is developed to perform vehicle classification and recognition. The effectiveness of the proposed approach is verified by outdoor experiments.
Language eng
Field of Research 090609 Signal Processing
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2010, Published by Elsevier Ltd.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30032318

Document type: Journal Article
Collection: School of Engineering
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