In this article, we observe that most false positive images (i.e., different identities with query images) in the top ranking list usually have the similar color information with the query image in person re-identification (Re-ID). Meanwhile, when we use the greyscale images generated from RGB images to conduct the person Re-ID task, some hard query images can obtain better performance compared with using RGB images. Therefore, RGB and greyscale images seem to be complementary to each other for person Re-ID. In this article, we aim to utilize both RGB and greyscale images to improve the person Re-ID performance. To this end, we propose a novel two-stream deep neural network with RGB-grey information, which can effectively fuse RGB and greyscale feature representations to enhance the generalization ability of Re-ID. First, we convert RGB images to greyscale images in each training batch. Based on these RGB and greyscale images, we train the RGB and greyscale branches, respectively. Second, to build up connections between RGB and greyscale branches, we merge the RGB and greyscale branches into a new joint branch. Finally, we concatenate the features of all three branches as the final feature representation for Re-ID. Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch. Besides, a global loss function is utilized to further fine-tune the final concatenated feature. The extensive experiments on multiple benchmark datasets fully show that the proposed method can outperform the state-of-the-art person Re-ID methods. Furthermore, using greyscale images can indeed improve the person Re-ID performance in the proposed deep framework.

中文翻译：

---

GreyReID：一种用于人员重新识别的具有 RGB 灰色信息的新型双流深度框架

在本文中，我们观察到排名靠前的大多数误报图像（即与查询图像不同的身份）通常在人员重新识别（Re-ID）中具有与查询图像相似的颜色信息。同时，当我们使用由 RGB 图像生成的灰度图像进行 person Re-ID 任务时，与使用 RGB 图像相比，一些硬查询图像可以获得更好的性能。因此，对于行人 Re-ID，RGB 和灰度图像似乎是相互补充的。在本文中，我们的目标是同时利用 RGB 和灰度图像来提高人员 Re-ID 性能。为此，我们提出了一种新颖的具有RGB-灰度信息的双流深度神经网络，它可以有效地融合RGB和灰度特征表示，以增强Re-ID的泛化能力。第一的，我们在每个训练批次中将 RGB 图像转换为灰度图像。基于这些 RGB 和灰度图像，我们分别训练 RGB 和灰度分支。其次，为了在 RGB 和灰度分支之间建立连接，我们将 RGB 和灰度分支合并为一个新的联合分支。最后，我们将所有三个分支的特征连接起来作为 Re-ID 的最终特征表示。此外，在训练过程中，我们采用联合学习方案，通过独立的损失函数同时训练每个分支，可以增强每个分支的泛化能力。此外，使用全局损失函数来进一步微调最终的连接特征。在多个基准数据集上进行的大量实验充分表明，所提出的方法可以优于最先进的人员 Re-ID 方法。