For intelligent transportation systems

vehicle recognition in complex traffic scenes is a key issue. In this article

a novel scheme using(HOG) features and sparse representation target recognition for vehicle recognition in complex traffic scenes is proposed. Our method uses the HOG to extract features from samples and candidate targets

and then wses trained samples as an overcomplete dictionary based on sparse representation. Finally

candidate targets are recognized by computing sparsity and reconstruction residuals in the dictionary. Experiment results show that the proposed scheme provides higher recognition preciseness in real time

even in complex traffic scenes such containing occlusion and a large variety of target classes.