Local perspective based synthesis for vehicle re-identification: A transformation state adversarial method

Vehicle re-identification (V-ReID) aims at discovering an image of a specific vehicle from a set of images typically captured by different cameras. Vehicles are one of the most important objects in cross-camera target recognition systems, and recognizing them is one of the most difficult tasks due to the subtle differences in the visible characteristics of vehicle rigid objects. Compared to various methods that can improve re-identification accuracy, data augmentation is a more straightforward and effective technique. In this paper, we propose a novel data synthesis method for V-ReID based on local-region perspective transformation, transformation state adversarial learning and a candidate pool. Specifically, we first propose a parameter generator network, which is a lightweight convolutional neural network, to generate the transformation states. Secondly, an adversarial module is designed in our work, it ensures that noise information is added as much as possible while keeping the labeling and structure of the dataset intact. With this adversarial module, we are able to promote the performance of the network and generate more proper and harder training samples. Furthermore, we use a candidate pool to store harder samples for further selection to improve the performance of the model. Our system pays more balanced attention to the features of vehicles. Extensive experiments show that our method significantly boosts the performance of V-ReID on the VeRi-776, VehicleID and VERI-Wild datasets. • A data augmentation method is designed that integrates a deep learning framework. • Local-region perspective transformation is optimized with an adversarial model. • More balanced attention is paid to the overall features of vehicles. • Data augmentation and the recognition model are jointly optimized. • Amplified samples are more efficient due to the automatic learning process. • A candidate pool stores the augmented images through the dynamic learning process. • Datasets are augmented by lifting the difficulty rather than the quantity. • The structure of the original dataset is reserved including the size and labels.