Prototype-Based Inter-Camera Learning for Person Re-Identification

Person re-identification (ReID) aims at retrieving images of the same person across non-overlapping camera views. The prior works focus on either fully supervised or unsupervised ReID settings, and achieve remarkable performances. In real scenarios, however, the major annotation cost comes from matching identity classes across camera views, thus leading to the Intra-Camera Supervised (ICS) ReID problem. In this work, we propose a Prototype-based Inter-camera ReID (PIRID) method, which tackles the ICS setting through the lens of prototype learning. Specifically, we first introduce the intra-camera learning with non-parametric classifiers to separately generate discriminative features within each camera view. Moreover, the inter-camera prototype learning provides prototypes as the representatives of each class in the common space, making the learned features to be camera-agnostic. Experiments conducted on three benchmarks, i.e., Market-1501, DukeMTMC-ReID, and MSMT17, show the superiority of our method.