A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Traffic Sign Detection and Recognition
This chapter presents an overview of traffic sign detection and recognition. It describes the characteristics of traffic signs and the requirements and difficulties when dealing with traffic sign detection and recognition in outdoor images. The chapter also covers the different techniques invoked to segment traffic signs from the different traffic scenes and the techniques employed for the recognition and classification of traffic signs. It points many problems regarding the stability of the received colour information, variations of these colours with respect to the daylight conditions and absence of a colour model that can lead to a good solution. It also proposes an adaptive colour segmentation model based on neural networks. The chapter demonstrates the way to classify segmented traffic signs by employing one of widely used classifiers, AdaBoost, based on a set of features, in this case HOG descriptors, which was developed for pedestrian recognition but found the way for many applications in different fields. The chapter ends by showing examples where traffic sign recognition is applicable in vehicle industry.
Traffic Sign Detection and Recognition
This chapter presents an overview of traffic sign detection and recognition. It describes the characteristics of traffic signs and the requirements and difficulties when dealing with traffic sign detection and recognition in outdoor images. The chapter also covers the different techniques invoked to segment traffic signs from the different traffic scenes and the techniques employed for the recognition and classification of traffic signs. It points many problems regarding the stability of the received colour information, variations of these colours with respect to the daylight conditions and absence of a colour model that can lead to a good solution. It also proposes an adaptive colour segmentation model based on neural networks. The chapter demonstrates the way to classify segmented traffic signs by employing one of widely used classifiers, AdaBoost, based on a set of features, in this case HOG descriptors, which was developed for pedestrian recognition but found the way for many applications in different fields. The chapter ends by showing examples where traffic sign recognition is applicable in vehicle industry.
Traffic Sign Detection and Recognition
Loce, Robert P. (editor) / Bala, Raja (editor) / Trivedi, Mohan (editor) / Fleyeh, Hasan (author)
2017-03-14
31 pages
Article/Chapter (Book)
Electronic Resource
English
Generalized Traffic Sign Detection Model for Developing a Sign Inventory
| Online Contents | 2009