TBE-Net: A Three-Branch Embedding Network With Part-Aware Ability and Feature Complementary Learning for Vehicle Re-Identification

Vehicle re-identification (Re-ID) is one of the promising applications in the field of computer vision. Existing vehicle Re-ID methods mainly focus on global appearance features or pre-defined local region features, which have difficulties in handling inter-class similarities and intra-class differences among vehicles in various traffic scenarios. This paper proposes a novel end-to-end three-branch embedding network (TBE-Net) with feature complementary learning and part-aware ability. The proposed TBE-Net integrates complementary features, global appearance, and local region features into a unified framework for subtle feature learning, thereby obtaining more integral and diverse vehicle features to re-identify the vehicle from similar ones. The local region feature branch in the proposed TBE-Net contains an attention module that highlights the major differences among local regions by adaptively assigning large weights to the critical local regions and small weights to insignificant local regions, thereby enhancing the perception sensitivity of the network to subtle discrepancies. The complementary branch in the proposed TBE-Net exploits different pooling operations to obtain more comprehensive structural features and multi-granularity features as a supplement to the global appearance and local region features. The abundant features help accommodate the ever-changing critical local regions in vehicles' images due to the sensors' settings, such as the position and shooting angle of surveillance cameras. The extensive experiments on VehicleID and VeRi-776 datasets show that the proposed TBE-Net outperforms the state-of-the-art methods.