Abstract
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).
Abstract (translated)
大多数视频监控系统同时使用RGB和红外摄像机，这使得重新识别穿过RGB和红外模式的人成为一项至关重要的技术。由于RGB和红外图像的异质性引起的交叉模态变化，以及由于人体姿态、相机视图、光线亮度等异质性引起的模态变化，这项任务可能具有挑战性。为了应对这些挑战，提出了一种新的特征学习框架hpiln。在该框架中，对现有的单模态重新识别模型进行了修改，以适应交叉模态场景，然后采用专门设计的硬五角形损失和身份损失来提高改进的交叉模态重新识别模型的性能。以SYSU-MM01数据集为基准，进行了大量的实验研究，结果表明，该方法在累积匹配特征曲线（CMC）和平均精度（MAP）方面优于现有方法。
URL
https://arxiv.org/abs/1906.03142
