A Novel Multi-Pose Person Re-Identification Method Based on Semantic- and Pose-Guided Feature Fusion

Person re-identification (ReID) aims to match the query images with images in the gallery. However, ReID traditionally focuses on outdoor scenes and standing pose, neglecting the complexities of different poses and indoor environments. These neglects correspond to some challenges: postural differences and background noise which interfere with feature learning and matching. To address these issues, we propose a novel multi-pose ReID method, Semantic- and Pose-Guided Feature Fusion (SPGFF), which integrates semantic-guided and pose-guided features. Specially, a semantic-guided module is employed to incorporate global contextual semantic information into the feature representation. This global contextual semantic information refers to the comprehensive understanding of the entire image, including the relationships of different pixels. By incorporating this information, even there are significant variations in pose, the model is enabled to focus on the most pertinent parts of the image. Meanwhile, the pose-guided module uses pose feature to cleanly disentangle bodily semantic components and selectively match corresponding body parts. To the best of our knowledge, this is the first work to introduce the concept of multi-pose ReID, we have provided a benchmark to address the issue of a lack of publicly available datasets. We demonstrate the effectiveness of our approach on both public and proprietary datasets, showcasing its potential to significantly improve person re-identification in previously overlooked scenarios.