TVG-ReID: Transformer-Based Vehicle-Graph Re-Identification

Abstract

Vehicle re-identification is the task of identifying the same vehicle in different environments and from different angles and cameras. It is more challenging than re-identification of humans: 1)small differences between vehicles of the same model make it difficult to capture their subtle characteristics; 2)vehicles of different types and colors may have similar characteristics from different viewpoints or external conditions. To address these challenges, we propose a TVG-ReID network, using a Transformer network to enhance features extracted from a CNN backbone network. A vehicle knowledge graph transfer method(Vehicle-Graph) is proposed, which treats each vehicle as a node in a graph, where simple information is transmitted through edges to constrain the distance of the nodes in a metric learning manner. Experiments on two vehicle re-identification datasets demonstrate the good performance of our proposed model.