Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Automatic Detection and Classification of Traffic Signs
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Detection and classification of road signs in natural environments
Neural Computing and Applications
Using self-organising maps in the detection and recognition of road signs
Image and Vision Computing
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
libCudaOptimize: an open source library of GPU-based metaheuristics
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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This paper presents a system for detecting and classifying road signs from video sequences in real time. A model-based approach is used in which a prototype of the sign to be detected is transformed and matched to the image using evolutionary techniques. Then, the sign detected in the previous phase is classified by a neural network. Our system makes extensive use of the parallel computing capabilities offered by modern graphics cards and the CUDA architecture for both detection and classification. We compare detection results achieved by GPU-based parallel versions of Differential Evolution and Particle Swarm Optimization, and classification results obtained by Learning Vector Quantization and Multi-layer Perceptron. The method was tested over two real sequences taken from a camera mounted on-board a car and was able to correctly detect and classify around 70% of the signs at 17.5 fps, a similar result in shorter time, compared to the best results obtained on the same sequences so far.