Real-time GPU based road sign detection and classification

Roberto Ugolotti*, Youssef S.G. Nashed, Stefano Cagnoni

*Corresponding author for this work

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

12 Scopus citations


This paper presents a system for detecting and classifying road signs from video sequences in real time. A model-based approach is used in which a prototype of the sign to be detected is transformed and matched to the image using evolutionary techniques. Then, the sign detected in the previous phase is classified by a neural network. Our system makes extensive use of the parallel computing capabilities offered by modern graphics cards and the CUDA architecture for both detection and classification. We compare detection results achieved by GPU-based parallel versions of Differential Evolution and Particle Swarm Optimization, and classification results obtained by Learning Vector Quantization and Multi-layer Perceptron. The method was tested over two real sequences taken from a camera mounted on-board a car and was able to correctly detect and classify around 70% of the signs at 17.5 fps, a similar result in shorter time, compared to the best results obtained on the same sequences so far.

Original languageEnglish (US)
Title of host publicationParallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings
Number of pages10
EditionPART 1
StatePublished - 2012
Event12th International Conference on Parallel Problem Solving from Nature, PPSN 2012 - Taormina, Italy
Duration: Sep 1 2012Sep 5 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7491 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th International Conference on Parallel Problem Solving from Nature, PPSN 2012


  • Differential Evolution
  • Learning Vector Quantization
  • Neural Networks
  • Particle Swarm Optimization
  • Road Sign Classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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