ABSTRACT Deep metric learning has become a general method for person re-identification (ReID) recently. Existing methods train ReID model with various loss functions to learn feature representation and identify pedestrian. However, the interaction between person features and classification vectors in the training process is rarely concerned. Distribution of pedestrian features will greatly affect convergence of the model and the pedestrian similarity computing in the test phase. In this paper, we formulate improved softmax function to learn pedestrian features and classification vectors. Our method applies pedestrian feature representation to be scattered across the coordinate space and embedding hypersphere to solve the classification problem. Then, we propose an end-to-end convolutional neural network (CNN) framework with improved softmax function to improve the performance of pedestrian features. Finally, experiments are performed on four challenging datasets. The results demonstrate that our work is competitive compared to the state-of-the-art.

中文翻译：

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用于人员重新识别的集中嵌入超球面特征学习

摘要 深度度量学习最近已成为行人重新识别（ReID）的通用方法。现有方法使用各种损失函数训练 ReID 模型来学习特征表示和识别行人。然而，很少有人关注训练过程中人物特征和分类向量之间的相互作用。行人特征的分布将极大地影响模型的收敛性和测试阶段的行人相似度计算。在本文中，我们制定了改进的 softmax 函数来学习行人特征和分类向量。我们的方法应用行人特征表示分散在坐标空间中并嵌入超球面来解决分类问题。然后，我们提出了一种端到端的卷积神经网络 (CNN) 框架，该框架具有改进的 softmax 函数，以提高行人特征的性能。最后，在四个具有挑战性的数据集上进行了实验。结果表明，与最先进的技术相比，我们的工作具有竞争力。