Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Traffic sign recognition using evolutionary adaboost detection and forest-ECOC classification
IEEE Transactions on Intelligent Transportation Systems
Extracting nonlinear features for multispectral images by FCMC and KPCA
Digital Signal Processing
Robust class similarity measure for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
IEEE Transactions on Intelligent Transportation Systems
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A constructive approach for finding arbitrary roots of polynomials by neural networks
IEEE Transactions on Neural Networks
Face recognition with lattice independent component analysis and extreme learning machines
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Extreme Learning Machines (ELM 2011) Hangzhou, China, December 6 – 8, 2011
Hi-index | 0.01 |
Traffic sign recognition is an important and active research topic of intelligent transport system. With a constant increasing of the training database size, not only the recognition accuracy, but also the computation complexity should be considered in designing a feasible recognition approach. In this paper, an effective and efficient algorithm based on a relatively new artificial neural network, extreme learning machine (ELM), is proposed for traffic sign recognition. In the proposed algorithm, the locally normalized histograms of the oriented gradient (HOG) descriptors, which are extracted from the traffic sign images, are used as the features and the inputs of the ELM classification model. Moreover, the ratio of feature's between-category to within-category sums of squares (BW) is designed as a feature selection criterion to improve the recognition accuracy and to decrease the computation burden. Application on a well known database, German traffic sign recognition benchmark (GTSRB) dataset, demonstrates the feasibility and efficiency of the proposed BW-ELM model.