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Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
TU Kaiserslautern, Kaiserslautern, Germany.
2013 (English)Report (Other academic)
Abstract [en]

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.

  HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.

Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna , 2013. , 18 p.
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2013:16
Keyword [en]
Traffic signs, HOG descriptors, Gentle AdaBoost, Classification
National Category
Computer Systems
Research subject
Komplexa system - mikrodataanalys
URN: urn:nbn:se:du-12610OAI: diva2:628409
Available from: 2013-06-13 Created: 2013-06-13 Last updated: 2016-02-12Bibliographically approved

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Fleyeh, Hasan
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