A New Method for Traffic Signs Classification Using Probabilistic Neural Networks

  • Hang Zhang
  • Dayong Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Traffic signs can provide drivers with very valuable information about the road, in order to make driving safer and easier. In recent years, traffic signs recognition has aroused wide interests in many scholars. It has two main parts– the detection and the classification. This paper presents a new method for traffic signs classification based on probabilistic neural networks (PNN) and Tchebichef moment invariants. It has two hierarchies: the first hierarchy classifier can coarsely classify the input image into one of indicative signs, warning signs or prohibitive signs according to its background color threshold; the second hierarchy classifiers including of three PNN networks can concretely identify traffic sign. The inputs of every PNN use the new developed Tchebichef moment invariants. The simulation results show that the two-hierarchy classifier can improve the classification ability meanwhile can use in real-time system.


Traffic Sign Probabilistic Neural Network Probability Density Function Hierarchy Classifier Color Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hang Zhang
    • 1
  • Dayong Luo
    • 1
  1. 1.School of Information Science & EngineeringCentral South UniversityChangshaChina

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