Multi-View Vehicle Image Generation Network for Vehicle Re-Identification

Vehicle re-identification is a technology that continuously tracks and identifies vehicles in different spatial domains, and playing a critical role in Space-Air-Ground-Sea Integrated Networks(SAGSIN). Viewpoint variation problem, that is vehicle appearance changes greatly under various viewpoints, makes vehicle re-identification challenging. To eliminate the negative effects of viewpoint variation, in this paper, we propose a Multi-View Vehicle Image Generation Network for Vehicle Re-Identification(MVIGN). MVIGN generate images with the same identity as the input vehicle image but with a different and controllable pose to solve viewpoint variation problem. Extensive experiments indicate using images generated by MVIGN to expand training set can improve the model accuracy and reduce the cost of manually collecting and labeling data.