Road Vehicle Detection and Classification Using Magnetic Field Measurement

Chen, X; Kong, X; Xu, M; Sandrasegaran, K; Zheng, J

Road Vehicle Detection and Classification Using Magnetic Field Measurement

Chen, X

Kong, X

Xu, M

Sandrasegaran, K

Zheng, J

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Publication Type:: Journal Article
Citation:: IEEE Access, 2019, 7 pp. 52622 - 52633
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Chen, X https://orcid.org/0000-0002-7357-5767	en_US
dc.contributor.author	Kong, X https://orcid.org/0000-0001-7855-858X	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Sandrasegaran, K https://orcid.org/0000-0003-1270-5756	en_US
dc.contributor.author	Zheng, J	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEEE Access, 2019, 7 pp. 52622 - 52633	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132333
dc.description.abstract	© 2013 IEEE. This paper presents a road vehicle recognition and classification approach for intelligent transportation systems. This approach uses a roadside installed low-cost magnetometer and associated data collection system. The system measures the magnetic field changing, detects passing vehicles, and recognizes vehicle types. We introduce Mel Frequency Cepstral Coefficients (MFCC) to analyze vehicle magnetic signals and extract it as vehicle feature with the representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 3-dimensional map algorithm using Vector Quantization (VQ) to classify vehicle magnetic features to 4 typical types of vehicles in Australian suburbs: sedan, van, truck, and bus. In order to train an accurate classifier, training samples are selected using the Dynamic Time Warping (DTW). The verification experiments show that our approach achieves a high level of accuracy for vehicle detection and classification.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2019.2908006	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Road Vehicle Detection and Classification Using Magnetic Field Measurement	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

© 2013 IEEE. This paper presents a road vehicle recognition and classification approach for intelligent transportation systems. This approach uses a roadside installed low-cost magnetometer and associated data collection system. The system measures the magnetic field changing, detects passing vehicles, and recognizes vehicle types. We introduce Mel Frequency Cepstral Coefficients (MFCC) to analyze vehicle magnetic signals and extract it as vehicle feature with the representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 3-dimensional map algorithm using Vector Quantization (VQ) to classify vehicle magnetic features to 4 typical types of vehicles in Australian suburbs: sedan, van, truck, and bus. In order to train an accurate classifier, training samples are selected using the Dynamic Time Warping (DTW). The verification experiments show that our approach achieves a high level of accuracy for vehicle detection and classification.

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http://hdl.handle.net/10453/132333