The misalignment of human images caused by pedestrian detection bounding box errors or partial occlusions is one of the main challenges in person Re-Identification (Re-ID) tasks. Previous local-based methods mainly focus on learning local features in predefined semantic regions of pedestrians, usually use local hard alignment methods or introduce auxiliary information such as key human pose points to match local features. These methods are often not applicable when large scene differences are encountered. Targeting to solve these problems, we propose a simple and efficient Local Sliding Alignment (LSA) strategy to dynamically align the local features of two images by setting a sliding window on the local stripes of the pedestrian. LSA can effectively suppress spatial misalignment and does not need to introduce extra supervision information. Then, we design a Global-Local Dynamic Feature Alignment Network (GLDFA-Net) framework, which contains both global and local branches. We introduce LSA into the local branch of GLDFA-Net to guide the computation of distance metrics, which can further improve the accuracy of the testing phase. Evaluation experiments on several mainstream evaluation datasets including Market-1501, DukeMTMC-reID, and CUHK03 show that our method has competitive accuracy over the several state-of-the-art person Re-ID methods. Additionally, it achieves 86.1% mAP and 94.8% Rank-1 accuracy on Market1501.

翻译：由于行人探测框框错误或部分隔离造成的人类图像错配是个人重新识别(Re-ID)任务中的主要挑战之一。以前的地方方法主要侧重于在行人预先定义的语义区学习当地特征,通常使用当地的硬对齐方法,或引入辅助信息,如关键的人造相点等关键人造相点等与当地特征相匹配。这些方法在遇到重大场点差异时往往不适用。为了解决这些问题,我们提出了一个简单而高效的地方滑动协调(LSA)战略,通过在行人的地方条纹上设置一个滑动窗口,动态地调整两种图像的当地特征。当地方法主要侧重于在行人预定义的语义区学习地方特征,通常使用当地硬对齐方法,或引入辅助信息,如关键人造相容点点等。我们将LSA引入GLDFA-1Net的地方分支,以指导远程度量度的计算,从而进一步提高测试阶段的准确性。在包括市场-1501号、DUCMMMMM1号在内的几个主流评估数据集上进行了评价实验,并展示了我们公司-DUDMMMMMMMDRR的准确度方法。