PAII: A Pose Alignment Network with Information Interaction for Person Re-identification

As an important part of intelligent surveillance systems, person re-identification (re-ID) has a wide range of application prospects in smart cities. However, due to occlusion, viewpoint variation, and background shift, the misalignment problem always decreases the re-ID systems' effects. To solve this problem, a pose alignment network with information interaction (PAII) is proposed. This approach consists of three cascaded modules. First, guided by a pretrained pose estimator, the backbone with a dual attention block is used to obtain local features corresponding to different pose keypoints along with the global feature. Then, a pose alignment module is constructed to group these local features into different parts and fuse them with a hyperparameter lambda, which provides the possibility to achieve semantic alignment. Finally, since different semantic features are extracted, an information interaction module consisting of graph attention layers is made to conduct message passing between different semantic features. All semantic features and the global feature are used to calculate the loss functions. Our approach considers multi-scale representations and information interaction of semantic features, which makes it more robust to misalignment problems. Thus, the proposed PAII method achieves better performance than most existing methods on multiple popular re-ID datasets.