Vehicle re-identification method based on global and local feature fusion

Abstract

Vehicle re-identification is still an issue worth discussing due to change of view, light and angle changes. At present, the key of vehicle reidentification research is to solve the problems that the difference between the same class is big and the difference between different classes is small. For the study, a vehicle re-identification method ground on view classification is proposed.The vehicle images are divided into four views, namely, front, rear, top and side, and local features are extracted by the segmented view information. Using the CNN feature extractor, the global feature representation with car ID attribute is learned. We have done experiments with the VERI-776 data sets. The mean average accuracy of 78.13% is obtained, which proves the effectiveness of the method.