Deep Learning-Based Vehicle Classification Using an Ensemble of Local Expert and Global Networks

Jong Taek Lee, Yunsu Chung

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

24 Scopus citations

Abstract

Vehicle classification has been a challenging problem because of pose variations, weather / illumination changes, inter-class similarity and insufficient training dataset. With the help of innovative deep learning algorithms and large scale traffic surveillance dataset, we are able to achieve high performance on vehicle classification. In order to improve performance, we propose an ensemble of global networks and mixture of K local expert networks. It achieved a mean accuracy of 97.92%, a mean precision of 92.98%, a mean recall of 90.24% and a Cohen Kappa score of 96.75% on unseen test dataset from the MIO-TCD classification challenge.

Original languageEnglish
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
PublisherIEEE Computer Society
Pages920-925
Number of pages6
ISBN (Electronic)9781538607336
DOIs
StatePublished - 22 Aug 2017
Event30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 - Honolulu, United States
Duration: 21 Jul 201726 Jul 2017

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2017-July
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
Country/TerritoryUnited States
CityHonolulu
Period21/07/1726/07/17

Fingerprint

Dive into the research topics of 'Deep Learning-Based Vehicle Classification Using an Ensemble of Local Expert and Global Networks'. Together they form a unique fingerprint.

Cite this