Traffic Sign Recognition Based on Kernel Sparse Representation

This paper proposes a novel approach based on scale invariant feature transform (SIFT) and kernel sparse representation for traffic sign recognition in complex traffic scenes. This module consists of several steps. In the first stage, SIFT is introduced for feature extraction from samples and test targets, respectively. The features are mapping to the kernel space. In the second stage, we construct an over-complete dictionary based on kernel sparse representation. Finally, traffic objects are recognized by computing sparseness and reconstruction residuals in the dictionary. Experiment results show that the proposed approach enhances the class discriminant ability using traffic features with higher recognition preciseness and robustness in complex traffic scenes compared with SVM, SRC.