An automatic road sign recognition system based on a computational model of human recognition processing

C. Y. Fang, C. S. Fuh, P. S. Yen, S. Cherng, S. W. Chen

Research output: Contribution to journalArticle

90 Citations (Scopus)

Abstract

This paper presents an automatic road sign detection and recognition system that is based on a computational model of human visual recognition processing. Road signs are typically placed either by the roadside or above roads. They provide important information for guiding, warning, or regulating the behaviors drivers in order to make driving safer and easier. The proposed recognition system is motivated by human recognition processing. The system consists of three major components: sensory, perceptual, and conceptual analyzers. The sensory analyzer extracts the spatial and temporal information of interest from video sequences. The extracted information then serves as the input stimuli to a spatiotemporal attentional (STA) neural network in the perceptual analyzer. If stimulation continues, focuses of attention will be established in the neural network. Potential features of road signs are then extracted from the image areas corresponding to the focuses of attention. The extracted features are next fed into the conceptual analyzer. The conceptual analyzer is composed of two modules: a category module and an object module. The former uses a configurable adaptive resonance theory (CART) neural network to determine the category of the input stimuli, whereas the later uses a configurable heteroassociative memory (CHAM) neural network to recognize an object in the determined category of objects. The proposed computational model has been used to develop a system for automatically detecting and recognizing road signs from sequences of traffic images. The experimental results revealed both the feasibility of the proposed computational model and the robustness of the developed road sign detection system.

Original languageEnglish
Pages (from-to)237-268
Number of pages32
JournalComputer Vision and Image Understanding
Volume96
Issue number2 SPEC. ISS.
DOIs
Publication statusPublished - 2004 Nov

Keywords

  • Cognitive model
  • Configurable adaptive resonance theory neural network
  • Configurable associative memory neural network
  • Road sign recognition
  • Sensory, perceptual, and conceptual analyzer
  • Spatiotemporal attentional neural network

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition

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