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• [P468] A Robust Multi-class Traffic Sign Detection and Classification System using Asymmetric and Symmetric features
  Author(s): Jialin Jiao, Zhong Zheng, Jungme Park, Yi Murphey
  
  

  Abstract
  In this paper we present our research in traffic sign detection and classification. Specifically we present a set of asymmetric Haar-like features that will be shown to be effective in reducing false alarm rates for traffic sign detection, and a robust multi-class traffic sign detection and classification system built based upon the stage-by-stage performance analysis of individual traffic sign detectors trained using Adaboost.

  
   

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