Person Re-Identification in Panoramic Views Based on Bayesian Transformers

The panoramic view cameras offer more broad perspectives and continuous information for person re-identification (ReID). However, the panoramic-view videos suffer from objects distortion and bring more occlusion due to the fixed or moving capture points. This paper proposes a novel Bayesian Transformer Network (BTN) to adaptively capture the occlusion clues as Bayesian prior to guide the discriminative pedestrian-related feature extraction in the high-occlusion scenes. The Bayesian prior is built via a pre-trained CNN, which could recognize different occluded scenarios based on the severeness of noisy backgrounds. Moreover, to fully explore the occlusion prior, we propose to embed the semantic labels into a well-designed transformer network. By fostering the collaborative occlusion clues between the person and background, our method could achieve outstanding performance on both public benchmarks and panoramic view videos, which verifies the advantages of our BTN framework over existing methods.