Dual Context-Aware Refinement Network for Person Search

Person search has recently gained increasing attention as the novel task of localizing and identifying a target pedestrian from a gallery of non-cropped scene images. Its performance depends on accurate person detection and re-identification simultaneously by learning effective representations. In this work, we propose a novel dual context-aware refinement network (DCRNet) for person search, which jointly explores two kinds of contexts including intra-instance context and inter-instance context to learn discriminative representation. Specifically, an intra-instance context module is designed to refine the representation for the bounding box of a pedestrian by leveraging its surrounding regions covering the same pedestrian and its accessories, which contain abundant complementary visual appearance of pedestrians. Moreover, an inter-instance context module is proposed to expand the instance-level feature for the bounding box of a pedestrian, by utilizing the rich scene contexts of neighboring co-travelers across images. These two modules are built on top of a joint detection and feature learning framework, i.e., Faster R-CNN. Extensive experimental results on two challenging datasets have demonstrated the effectiveness of DCRNet with significant performance improvements over state-of-the-art methods.