In this paper, we propose a novel data augmentation method to dynamically learn occluded samples via adversarial learning for person re-identification (re-ID) in sensor networks. Specifically, we design two CNN models to learn original-image features and occluded-image features, respectively. As for occluded-image features, we extract the most salient region from the attention map to obtain the meaningful occluded region. To match the CNN status, we dynamically occlude pedestrian images in each iteration and meanwhile generate training pedestrian images with high diversity. We also employ adversarial learning to improve the generalization ability of CNN model. A discriminator is introduced to distinguish original-image features and occluded-image features, and occluded-image features are optimized to confuse the discriminator. As a result, the representations for pedestrian images contain the discriminative complementary information. We conduct extensive experiments on Market1501, DukeMTMC-reID and CUHK03, and the experimental results verify that the proposed method exceeds the state-of-the-art methods by a large margin.

在本文中，我们提出了一种新颖的数据扩充方法，通过对抗性学习来动态学习被遮挡的样本，以进行传感器网络中的人员重新识别（re-ID）。具体来说，我们设计了两个CNN模型来分别学习原始图像特征和遮挡图像特征。至于遮挡图像特征，我们从注意力图中提取出最显着的区域以获得有意义的遮挡区域。为了匹配CNN状态，我们在每次迭代中动态遮挡行人图像，同时生成具有高度多样性的训练行人图像。我们还采用对抗学习来提高CNN模型的泛化能力。引入鉴别器以区分原始图像特征和遮挡图像特征，并且遮挡图像特征被优化以混淆鉴别器。结果是，行人图像的表示包含判别性补充信息。我们在Market1501，DukeMTMC-reID和CUHK03上进行了广泛的实验，实验结果证明了该方法大大超过了现有技术。