Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re-identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross-modality variations caused by heterogeneous images in RGB and infrared, and the intra-modality variations caused by the heterogeneous human poses, camera views, light brightness, etc. To meet these challenges a novel feature learning framework, HPILN, is proposed. In the framework existing single-modality re-identification models are modified to fit for the cross-modality scenario, following which specifically designed hard pentaplet loss and identity loss are used to improve the performance of the modified cross-modality re-identification models. Based on the benchmark of the SYSU-MM01 dataset, extensive experiments have been conducted, which show that the proposed method outperforms all existing methods in terms of Cumulative Match Characteristic curve (CMC) and Mean Average Precision (MAP).

该论文提出了一种新的特征学习框架HPILN，它使用特别设计的hard pentaplet损失和身份损失来改善现有单模态重新识别模型以适应跨模态情景；该方法在SYSU-MM01数据集上的广泛实验显示，它在CMC曲线和MAP方面优于所有现有方法。

HPILN: 一种跨模态人员重新识别特征学习框架