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Traffic Sign Detection

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Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

To detect and classify objects contained in real images, acquired in unconstrained environments, is a challenging problem in computer vision, which complexity makes unfeasible the design of handcrafted solutions. In this chapter, the object detection problem is introduced, highlighting the main issues and challenges, and providing a basic introduction to the main concepts. Once the problem is formulated, a feature based approach is adopted for traffic sign detection, introducing the basic concepts of the machine learning framework and some bio-inspired features. Learning algorithms are explained in order to obtain good detectors using a rich description of traffic sign instances. Using the context of classical windowing detection strategies, this chapter introduces an evolutionary approach to feature selection which allows building detectors using feature sets with large cardinalities.

Keywords

Traffic sign detection Haar-like features Integral image Adaboost detection Cascade of classifiers Evolutionary computation 

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Copyright information

© Sergio Escalera 2011

Authors and Affiliations

  1. 1.Department of Applied Mathematics and AnalysisUniversity of BarcelonaBarcelonaSpain
  2. 2.Department of Computer ScienceUniversitat Oberta de CatalunyaBarcelonaSpain

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