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.

中文翻译：

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双向中心约束可见光热行人重识别排名靠前


可见热人员重新识别（VT-REID）是匹配热成像摄像机和可见光摄像机捕获的人员图像的任务，这在夜间监控应用中是一个极其重要的问题。现有的跨模态识别工作主要集中于学习可共享的特征表示以处理跨模态差异。然而，除了不同相机光谱引起的跨模态差异外，VT-REID还面临着不同相机环境和人体姿势等引起的较大的跨模态和模内变化。在本文中，我们提出了一种具有新颖的双向双约束顶级（BDTR）损失的双路径网络来学习判别性特征表示。它具有两个方面的特点：1）端到端学习，无需额外的度量学习步骤；2）双约束同时处理跨模态和模内变化以确保特征可辨别性。同时，提出了双向中心约束顶级排序（eBDTR），将前两个约束合并到单个公式中，该公式保留了处理跨模态和模态内变化的属性。对两个跨模态重识别数据集的广泛实验证明了所提出的方法与最先进的方法相比的优越性。