A novel approach based on scale-invariant feature transform (SIFT) and kernel sparse representation for traffic object recognition in complex traffic scenes is proposed in this paper.First

SIFT is introduced for feature extraction from samples and test targets

respectively.The features are mapping to the kernel space

then we construct an over-complete dictionary based on kernel sparse representation

traffic objects are recognized by computing sparsity and reconstruction residuals in the dictionary.We also analyze the relationship between recognition rate and dimensionality reduction of the SIFT descriptor using random projection.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.