Pedestrian occlusion, variations in the cross-view angle, and the appearances of pedestrians significantly hinder person reidentification (ReID). A dual attention and part drop network (DAPD-Net) for person ReID is proposed. The dual attention module enables the deep neural network to focus on the pedestrian in the foreground of a given image and weakens background perturbance. It can speed up learning and improve network performance. Feature maps in the part drop branch that we proposed are divided into multiple parts, one of them is randomly dropped, and the remainder are learned to obtain a feature that is robust against occlusion. Through part drop training, the antiocclusion ability of the network is effectively improved. The middle-layer branch is used, which help our network to learn mid-level semantic feature and promote capability of the system. These innovative modules can help deep neural network to extract discriminative feature representations. We conduct extensive experiments on multiple public datasets of person ReID. The results show that our method outperforms many state-of-the-art methods.

中文翻译：

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双重关注和零件掉落网络，用于人员识别

行人遮挡，交叉视角的变化以及行人的出现严重阻碍了人员识别（ReID）。提出了针对人ReID的双重注意和部分掉落网络（DAPD-Net）。双重关注模块使深度神经网络能够将注意力集中在给定图像前景的行人上，并减弱背景干扰。它可以加快学习速度并提高网络性能。我们在零件放置分支中提出的特征图被分为多个部分，其中之一被随机放置，其余部分被学习以获得对遮挡具有鲁棒性的特征。通过丢包训练，有效提高了网络的抗阻塞能力。使用中间层分支，这有助于我们的网络学习中层语义特征并提升系统功能。这些创新的模块可以帮助深度神经网络提取判别性特征表示。我们对人ReID的多个公共数据集进行了广泛的实验。结果表明，我们的方法优于许多最新方法。