In order to make full use of structural information of grayscale images and reduce adverse impact of illumination variation for person re-identification (ReID), an effective data augmentation method is proposed in this paper, which includes Random Grayscale Transformation, Random Grayscale Patch Replacement and their combination. It is discovered that structural information has a significant effect on the ReID model performance, and it is very important complementary to RGB images ReID. During ReID model training, on the one hand, we randomly selected a rectangular area in the RGB image and replace its color with the same rectangular area grayscale in corresponding grayscale image, thus we generate a training image with different grayscale areas; On the other hand, we convert an image into a grayscale image. These two methods will reduce the risk of overfitting the model due to illumination variations and make the model more robust to cross-camera. The experimental results show that our method achieves a performance improvement of up to 3.3%, achieving the highest retrieval accuracy currently on multiple datasets.

中文翻译：

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有效的数据扩充，用于人员重新识别

为了充分利用灰度图像的结构信息，减少光照变化对人身识别的不利影响，提出了一种有效的数据增强方法，包括随机灰度变换，随机灰度斑块替换和随机灰度变换。他们的组合。发现结构信息对ReID模型性能具有重要影响，并且它是对RGB图像ReID的非常重要的补充。在ReID模型训练中，一方面，我们随机选择RGB图像中的一个矩形区域，并在相应的灰度图像中用相同的矩形区域灰度替换其颜色，从而生成具有不同灰度区域的训练图像。另一方面，我们将图像转换为灰度图像。这两种方法将减少由于光照变化而导致模型过拟合的风险，并使模型对跨相机的鲁棒性更高。实验结果表明，我们的方法将性能提高了3.3％，在多个数据集上实现了目前最高的检索精度。