Person re-identification aims to match person captured by multiple non-overlapping cameras that mainly mean standard RGB cameras. In contemporary surveillance, cameras of different modalities such as infrared cameras and depth cameras are introduced because of their unique advantages in poor illumination scenarios. However, re-identifying the persons across such cameras of different modalities is extremely difficult and, unfortunately, seldom discussed. It is mainly caused by extremely different appearances of the person shown under such different camera modalities. In this paper, we tackle this challenging cross-modality people re-identification through a top-push constrained modality-adaptive dictionary learning. The proposed model asymmetrically projects the heterogeneous features from dissimilar modalities onto a common space. In this way, the modality-specific bias is mitigated. Thus, the heterogeneous data can be simultaneously enforced by a shared dictionary in a canonical space. Moreover, a top-push ranking graph regularization is embedded in the proposed model to improve the discriminability, which efficiently further boosts the matching accuracy. In order to implement the proposed model, an iterative process is developed in this paper to optimize these two processes jointly. Extensive experiments on the benchmark SYSU-MM01 and BIWI RGBD-ID person re-identification datasets show promising results which outperform state-of-the-art methods.

中文翻译：

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用于跨模态人重识别的顶推约束模态自适应字典学习

行人重识别旨在匹配由多个非重叠摄像机捕获的人，主要是指标准 RGB 摄像机。在现代监控中，红外摄像机和深度摄像机等不同模态的摄像机因其在光线不足的情况下具有独特的优势而被引入。然而，通过这些不同模式的相机重新识别人是极其困难的，不幸的是，很少讨论。这主要是由于在这种不同的相机模式下显示的人的外观极其不同。在本文中，我们通过顶推约束模态自适应字典学习来解决这一具有挑战性的跨模态人员重新识别问题。所提出的模型不对称地将不同模态的异质特征投影到公共空间上。这样，减轻了特定于模态的偏见。因此，异构数据可以由规范空间中的共享字典同时强制执行。此外，在所提出的模型中嵌入了顶推排名图正则化以提高可辨别性，从而有效地进一步提高了匹配精度。为了实现所提出的模型，本文开发了一个迭代过程来联合优化这两个过程。在基准 SYSU-MM01 和 BIWI RGBD-ID 人员重新识别数据集上进行的大量实验显示出优于最先进方法的有希望的结果。在所提出的模型中嵌入了顶推排名图正则化以提高可辨别性，从而有效地进一步提高了匹配精度。为了实现所提出的模型，本文开发了一个迭代过程来联合优化这两个过程。在基准 SYSU-MM01 和 BIWI RGBD-ID 人员重新识别数据集上进行的大量实验显示出优于最先进方法的有希望的结果。在所提出的模型中嵌入了顶推排名图正则化以提高可辨别性，从而有效地进一步提高了匹配精度。为了实现所提出的模型，本文开发了一个迭代过程来联合优化这两个过程。在基准 SYSU-MM01 和 BIWI RGBD-ID 人员重新识别数据集上进行的大量实验显示出优于最先进方法的有希望的结果。