RT Conference Proceedings
T1 Testing feature selection in traffic signs
A1 Sesmero Lorente, María Paz
A1 Alonso Weber, Juan Manuel
A1 Gutiérrez Sánchez, Germán
A1 Sanchis de Miguel, María Araceli
AB Road signs carry essential information for successful driving. Therefore, if we are interested in developing a Driver Support Systems, both, detection and classification of road signs are essential tasks for an  autonomous system. However, both tasks are some of the less studied subjects in the field of Intelligent Transport systems. In this research we lay the foundations of a software implementation for a classifier system that will be implemented in hardware and will be able to be used for real-time traffic sign categorization. The selected classification method is a Multilayer Perceptron trained with Back-Propagation algorithm. The reason of this selection is, on one hand, that for certain types of problems, such as object recognition in natural environments, neural network learning methods provide a robust approach. On the other hand, and under certain, limitations related mainly to the number of units, a hardware implementation on FPGA of ANN is possible. Therefore, ANNs are a good method for real-time processing in real-word problems
PB Universidad de Las Palmas de Gran Canaria
SN 978-84-690-3603-7
YR 2007
FD 2007
LK https://hdl.handle.net/10016/12177
UL https://hdl.handle.net/10016/12177
LA spa
NO This work has been supported by the Spanish Government under project TRA2004-07441-C03-C01
DS e-Archivo
RD 17 jul. 2024