Abstract
As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping multiple cameras or across different time with a single camera. Recently, deep learning-based person re-identification models have achieved great success in many benchmarks. However, these supervised models require a large amount of labeled image data, and the process of manual labeling spends much manpower and time. In this study, we introduce a method to automatically synthesize labeled person images and adopt them to increase the sample number per identity for person re-identification datasets. To be specific, we use block rectangles to randomly occlude pedestrian images. Then, a generative adversarial network (GAN) model is proposed to use paired occluded and original images to synthesize the de-occluded images that similar but not identical to the original image. Afterwards, we annotate the de-occluded images with the same labels of their corresponding raw images and use them to augment the number of samples per identity. Finally, we use the augmented datasets to train baseline model. The experiment results on CUHK03, Market-1501 and DukeMTMC-reID datasets show that the effectiveness of the proposed method.
Abstract (translated)
作为多摄像机监控系统的一项基本任务，人的再识别是用一台摄像机对从非重叠多台摄像机或跨不同时间观测到的查询行人进行再识别。近年来，基于深度学习的人再识别模型在许多基准测试中取得了巨大的成功。然而，这些监控模型需要大量的标记图像数据，人工标记的过程耗费了大量的人力和时间。在本研究中，我们引入了一种自动合成标记人物图像的方法，并采用这些方法来增加每个身份的样本数，以用于重新识别人物数据集。具体来说，我们使用块矩形来随机遮挡行人图像。然后，提出了一种生成对抗网络（gan）模型，利用成对遮挡图像和原始图像合成与原始图像相似但不相同的非遮挡图像。然后，我们用它们对应的原始图像的相同标签来注释被消除的图像，并使用它们来增加每个标识的样本数量。最后，我们使用增强数据集来训练基线模型。在CUHK03、Market-1501和Dukemtmc REID数据集上的实验结果表明了该方法的有效性。
URL
https://arxiv.org/abs/1809.09970
