Sparse representation method of vehicle recognition in complex traffic scenes
复杂交通场景中采用稀疏表示的车辆识别方法

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.