Multi-branch network with hierarchical bilinear pooling for person reidentification

Because of issues such as viewpoint changes, posture variations, and background cluttering, the task of person reidentification (Re-ID) remains challenging. The model of combining global features and part features has been widely used in person Re-ID technology in recent years, but these efforts ignored feature interaction between the convolutional layers and thus lost detailed information conducive to identifying pedestrians under different cameras. To achieve interaction between hierarchical features, a multibranch network with hierarchical bilinear pooling (MBN-HBP) is proposed. The network consists of a global branch, a part-level branch, and a hierarchical bilinear pooling (HBP) branch. The person features extracted by the network include not only global and part-level features but also detailed HBP features. The final feature representation will be more robust to deal with the complex surveillance environment. By conducting comprehensive experiments, competitive performance on the Market-1501, DukeMTMC-Re-ID, and CUHK03 datasets is obtained.