Traffic-Sign Recognition Systems
Springer Science & Business Media, Sep 22, 2011 - Computers - 96 pages
This work presents a full generic approach to the detection and recognition of traffic signs. The approach, originally developed for a mobile mapping application, is based on the latest computer vision methods for object detection, and on powerful methods for multiclass classification. The challenge was to robustly detect a set of different sign classes in real time, and to classify each detected sign into a large, extensible set of classes. To address this challenge, several state-of-the-art methods were developed that can be used for different recognition problems. Following an introduction to the problems of traffic sign detection and categorization, the text focuses on the problem of detection, and presents recent developments in this field. The text then surveys a specific methodology for the problem of traffic sign categorization – Error-Correcting Output Codes – and presents several algorithms, performing experimental validation on a mobile mapping application. The work ends with a discussion on future lines of research, and continuing challenges for traffic sign recognition.
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3 Traffic Sign Detection
4 Traffic Sign Categorization
5 Traffic Sign Detection and Recognition System
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4-class problem accuracy Adaboost algorithm applied approach attentional cascade base classifier binary classifiers binary problems boosting class C1 codeword coding matrix color computer vision corresponds database decision boundaries decision stumps DECOC defined detection and recognition detector dichotomizer discriminative dissociated dipoles distance ECOC ECOC design ECOC matrix ECOC-ONE Error-Correcting Output Codes Euclidean Decoding evolutionary example Forest-ECOC function Genetic Algorithms Haar wavelet Haar-like features Hamming Decoding IEEE Trans input Intell labels linear classifier Linear Discriminant Analysis logistic regression machine learning methods multi-class neural network node object detection object recognition obtained optimal trees parameters partition pattern recognition performance pixels regions of interest representation road signs robust samples set of classes shown in Fig splitting step strategy sub-classes sub-partitions of classes sub-sets Support Vector Machines techniques traffic sign detection traffic sign recognition training set Update variance visual wavelet weak learning algorithm weight