Multi-feature vehicle detection using feature selection

Chungsu Lee, Jonghee Kim, Eunsoo Park, Jonghwan Lee, Hakil Kim, Junghwan Kim, Hyojin Kim

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

4 Scopus citations

Abstract

Feature selection has received attention recently in the field of object detection. A vehicle detection method using feature selection is presented in this work. An efficient feature subset is selected using feature selection methods and each feature subset is evaluated by computing the average error rate in different classification methods. The feature selection methods used in this work are the logistic regression, least absolute shrinkage and selection operator (LASSO) and the random forest (RF) methods. The proposed method is evaluated using actual data, showing good performance.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Pages234-238
Number of pages5
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom
Duration: 13 Oct 201316 Oct 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

Conference

Conference2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Country/TerritoryUnited Kingdom
CityManchester
Period13/10/1316/10/13

Keywords

  • Feature selection
  • Multi feature
  • Vehicle detection

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