Orientation-Guided Similarity Learning for Person Re-identification

Person re-identification (re-id) is a promising topic in computer vision, which concentrates on similarity learning of individuals across different camera views. It remains challenging due to the unpredictable orientation variations, the partial occlusions, and the inaccurate detections. To solve these problems, we present an orientation-guided similarity learning architecture to learn discriminative feature representations and define similarity metric for person re-id. Our proposed architecture explicitly leverages pedestrian orientation and body part cues to enhance the generalization ability. In the architecture, an orientation-guided loss function that pulls the positive samples with the same orientations closer is designed to alleviate the orientation variations. Meanwhile, an aligned dense network with pose estimation is presented to extract robust global-local fusion representations, which effectively exploits local features to overcome partial occlusions. In the end, we introduce a two-stage Top-k reranking strategy to optimize initial re-id results by min-hash and weighted distance. Extensive experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art re-id methods on the popular CUHK03, Market1501, and DukeMTMC-reID datasets.