Semantic-Aware Detail Search and Feature Constraint for Cross-Resolution Person Re-Identification

Cross-resolution person re-identification (CRReID) task devotes to identifying the same person from cross-resolution and cross-camera images. Existing CRReID methods learn the identity features of persons by jointly training the super-resolution (SR) and recognition models. These methods achieve sub-optimal results because the design of SR techniques is mostly oriented towards the visual quality of images rather than recognition tasks. To address this deficiency, we propose a Semantic-Aware detail Search and feature Constraint Network (SASC-Net). Specifically, we propose the semantic-aware detail search (SDS) module that is used to customize an SR module by perceiving identity-related semantic information. Then, we devise an Intra-Scale and Inter-Scale Feature Constraint loss function. It ensures that the affinity relationships of the semantic features of repaired images are close to that of high-resolution (HR) images at the scale level, reducing the solution space of the SDS module and promoting the identification module to focus on more discriminative pedestrian features. The effectiveness of our proposed method is validated by the experimental results on five cross-resolution person datasets.