Intra color-shape classification for traffic sign recognition

King Hann Lim, Kah Phooi Seng, Li Minn Ang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

28 Citations (Scopus)


This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural network (RBFNN). In the proposed system, traffic signs are first segmented and classified with regard to its unique color and shape in order to partition a large set of data into smaller subclasses. Within these subclasses, all redundant information except the pictogram is discarded for feature selection since the pictogram contains critical information for road users. Principle Component Analysis (PCA) is applied to extract salient points for traffic sign dimensionality reduction. This is followed by the Fisher's Linear Discriminant (FLD) to further obtain the most discriminant features. These features are fed into RBFNN for training with a proposed weight updating scheme based on Lyapunov stability theory. The performance of the proposed system is evaluated with Malaysian road signs with promising recognition rate.

Original languageEnglish
Title of host publicationICS 2010 - International Computer Symposium
Number of pages6
Publication statusPublished - 2010
Externally publishedYes
Event2010 International Computer Symposium, ICS 2010 - Tainan, Taiwan, Province of China
Duration: 16 Dec 201018 Dec 2010

Publication series

NameICS 2010 - International Computer Symposium


Conference2010 International Computer Symposium, ICS 2010
Country/TerritoryTaiwan, Province of China


  • Advanced driver assistance system
  • Classificaiton
  • Traffic sign recognition

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