Traffic-Sign Recognition Systems

This work presents a full generic approach to the detection and recognition of traffic signs. 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. 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.

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. To address this challenge, several state-of-the-art methods were developed that can be used for different recognition problems. 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.This work presents a full generic approach to the detection and recognition of traffic signs. The approach 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 research and continuing challenges.