Vehicle re-identification based on grouping aggregation attention and cross-part interaction

Abstract

Vehicle re-identification (Re-ID) plays an important role in intelligent transportation systems. To solve the key problem of small inter-class difference, we propose a vehicle Re-ID network based on grouping aggregation attention and cross-part interaction (GACP), which uses a channel context local interaction attention block (CCLI) and a cross-part interaction module (CPIM) to extract global and local discriminative features, respectively. The CCLI constructs the context information of each channel and performs local interaction to realize the communication of local and long-range information, thus effectively infer the weights of channels. In addition, the CCLI further reduces the number of parameters and improves the attention effect through a grouping aggregation module and an attention enhancement constraint. The CPIM enhances the correlation between vehicle parts obtained by rigid segmentation on feature maps to mine robust local information. Extensive experiments on two popular datasets VeRi776 and VehicleID demonstrate the effectiveness of the proposed method.