Application of soft Histogram of Oriented Gradient on traffic sign detection

Peyman Hosseinzadeh Kassani, Junhyuk Hyun, Euntai Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The proposal of this study is introducing a new feature, called soft Histogram of Oriented Gradients (SHOG). This feature is designed for traffic sign detection. SHOG differs from traditional (hard) HOG in terms of symmetry information and cell of histogram positions. Unlike hard HOG, SHOG changes the positions of cells to a randomized selection of cells following by symmetry shapes of the traffic sign images. SHOG is implemented on the famous German traffic sign detection benchmark (GTSDB) dataset. Comparing to the conventional HOG feature experimented on GTSDB, SHOG could show better performance while uses smaller feature size.

Original languageEnglish
Title of host publication2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages388-392
Number of pages5
ISBN (Electronic)9781509008216
DOIs
Publication statusPublished - 2016 Oct 21
Event13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016 - Xian, China
Duration: 2016 Aug 192016 Aug 22

Publication series

Name2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016

Other

Other13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016
CountryChina
CityXian
Period16/8/1916/8/22

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Artificial Intelligence
  • Control and Optimization

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  • Cite this

    Kassani, P. H., Hyun, J., & Kim, E. (2016). Application of soft Histogram of Oriented Gradient on traffic sign detection. In 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016 (pp. 388-392). [7734067] (2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/URAI.2016.7734067