In recent years, deep learning (DL) has been successfully and widely applied in the person reidentification (Re-ID). However, the DL-based person Re-ID methods face a bottleneck that the scales of most existing person Re-ID databases are not large enough for training very deep models. To address this problem, a body symmetry and part-locality-guided direct nonparametric deep feature enhancement (DNDFE) method is proposed in this article. Based on the observation that the body symmetry and part locality are two important appearance properties inherited in the upright walking persons, the proposed method designs two nonparametric layers, namely, the body symmetry average pooling and local normalization layers, to construct a DNDFE module to well explore the body symmetry and part locality properties. The proposed DNDFE module could be directly embedded between the traditional deep feature learning module and similarity learning module to enhance the DL features so as to improve the person Re-ID performance. The experimental results have shown that the proposed DNDFE method is superior to multiple state-of-the-art person Re-ID methods in terms of accuracy and efficiency.

中文翻译：

---

身体对称性和零件局部性引导的直接非参数深层特征增强，用于人员重新识别

近年来，深度学习（DL）已成功且广泛地应用于人员识别（Re-ID）。但是，基于DL的人员Re-ID方法面临一个瓶颈，即大多数现有人员Re-ID数据库的规模不足以训练非常深的模型。为了解决这个问题，本文提出了一种基于身体对称性和局部局部性的直接非参数深特征增强（DNDFE）方法。在观察到人体对称性和局部局部性是直立步行者继承的两个重要的外观特性的基础上，该方法设计了两个非参数层，即人体对称性平均汇聚层和局部归一化层，以构造出良好的DNDFE模块。探索身体的对称性和局部局部性。提出的DNDFE模块可以直接嵌入传统的深度特征学习模块和相似度学习模块之间，以增强DL特征，从而提高人的Re-ID性能。实验结果表明，所提出的DNDFE方法在准确性和效率上均优于多种最新的人Re-ID方法。