Vehicle classification using road side sensors and feature-free data smashing approach

Denis Kleyko, Roland Hostettler, Nikita Lyamin, Wolfgang Birk, Urban Wiklund, Evgeny Osipov

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)
170 Downloads (Pure)

Abstract

The main contribution of this paper is a study of the applicability of data smashing -A recently proposed data mining method - for vehicle classification according to the "Nordic system for intelligent classification of vehicles" standard, using measurements of road surface vibrations and magnetic field disturbances caused by passing vehicles. The main advantage of the studied classification approach is that it, in contrast to the most of traditional machine learning algorithms, does not require the extraction of features from raw signals. The proposed classification approach was evaluated on a large dataset consisting of signals from 3074 vehicles. Hence, a good estimate of the actual classification rate was obtained. The performance was compared to the previously reported results on the same problem for logistic regression. Our results show the potential trade-off between classification accuracy and classification method's development efforts could be achieved.

Original languageEnglish
Title of host publication2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
PublisherIEEE
Pages1988-1993
Number of pages6
ISBN (Electronic)9781509018895
DOIs
Publication statusPublished - 22 Dec 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Intelligent Transportation Systems - Rio de Janeiro, Brazil
Duration: 1 Nov 20164 Nov 2016
Conference number: 19

Publication series

NameProceedings of the IEEE International Conference on Intelligent Transportation Systems
ISSN (Electronic)2153-0017

Conference

ConferenceIEEE International Conference on Intelligent Transportation Systems
Abbreviated titleITSC
Country/TerritoryBrazil
CityRio de Janeiro
Period01/11/201604/11/2016

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