Vehicle logo super-resolution by canonical correlation analysis
Recognition of a vehicle make is of interest in the fields of law enforcement and surveillance. In this paper, we develop a canonical correlation analysis (CCA) based method for vehicle logo super-resolution to facilitate the recognition of the vehicle make. From a limited number of high-resolution logos, we populate the training dataset for each make using gamma transformations. Given a vehicle logo from low-resolution source (i.e., surveillance or traffic camera recordings), the learned models yield super-resolved results. By matching the low-resolution image and the generated high-resolution images, we select the final output that is closest to the low-resolution image in the histogram of oriented gradients (HOG) feature space. Experimental results show that our approach outperforms the state-of-the-art super-resolution methods in qualitative and quantitative measures. Furthermore, the super-resolved logos help to improve the accuracy in the subsequent recognition tasks significantly.