Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition

This paper presents a quantitative comparison of several segmentation methods (including new ones) that have successfully been used in traffic sign recognition. The methods presented can be classified into color-space thresholding, edge detection, and chromatic/achromatic decomposition. Our support vector machine (SVM) segmentation method and speed enhancement using a lookup table (LUT) have also been tested. The best algorithm will be the one that yields the best global results throughout the whole recognition process, which comprises three stages: 1) segmentation; 2) detection; and 3) recognition. Thus, an evaluation method, which consists of applying the entire recognition system to a set of images with at least one traffic sign, is attempted while changing the segmentation method used. This way, it is possible to observe modifications in performance due to the kind of segmentation used. The results lead us to conclude that the best methods are those that are normalized with respect to illumination, such as RGB or Ohta Normalized, and there is no improvement in the use of Hue Saturation Intensity (HSI)-like spaces. In addition, an LUT with a reduction in the less-significant bits, such as that proposed here, improves speed while maintaining quality. SVMs used in color segmentation give good results, but some improvements are needed when applied to achromatic colors.

• Record URL:
  https://doi.org/10.1109/TITS.2010.2054084
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • Gomez-Moreno, Hilario
  • Maldonado-Bascon, Saturino
  • Gil-Jimenez, Pedro
  • Lafuente-Arroyo, Sergio
• Publication Date: 2010

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 917-930
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 11
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Computer vision; Edge detection; Image analysis; Mathematical models; Traffic signs; Vector analysis
• Uncontrolled Terms: Traffic sign recognition
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I20: Design and Planning of Transport Infrastructure;

Filing Info

• Accession Number: 01333632
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: TLIB
• Created Date: Mar 21 2011 2:15PM