Bi Directional Center Constrained Top Ranking for Visible Thermal Person Re Identification
Visible thermal person re identification (VT REID) is a task of matching person images captured by thermal and visible cameras, which is an extremely important issue in night time surveillance applications. Existing cross modality recognition works mainly focus on learning sharable feature representations to handle the cross modality discrepancies. However, apart from the cross modality discrepancy caused by different camera spectrums, VT REID also suffers from large cross modality and intra modality variations caused by different camera environments and human poses, and so on. In this paper, we propose a dual path network with a novel bi directional dual constrained top ranking (BDTR) loss to learn discriminative feature representations. It is featured in two aspects: 1) end to end learning without extra metric learning step and 2) the dual constraint simultaneously handles the cross modality and intra modality variations to ensure the feature discriminability. Meanwhile, a bi directional center constrained top ranking (eBDTR) is proposed to incorporate the previous two constraints into a single formula, which preserves the properties to handle both cross modality and intra modality variations. The extensive experiments on two cross modality re ID datasets demonstrate the superiority of the proposed method compared to the state of the arts.
IEEE Transactions on Information Forensics and Security