Most existing person re-identification methods are mainly based on human part partition with horizontal stripes or human body semantic segmentation. In this paper, we propose a method called MDRS (Multi-scale Discriminative network with Region Segmentation) to integrate multi-scale discriminative feature learning, horizontal stripe partition and semantic segmentation in a single framework, in which multi-scale horizontal stripe partition and usage of both global and local features make the framework be robust to human pose variation, occlusion and background clutter, and semantic segmentation boosts the performance of person identification via shared multi-scale feature extraction. MDRS is trained end-to-end with a multi-task learning strategy that considers three tasks simultaneously: person identification, triplet prediction and pixel-wise semantic segmentation. Comprehensive experiments confirm that our approach exceeds many methods and robustly achieves excellent performances on mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03.

现有的大多数人的重新识别方法主要是基于具有水平条纹的人体部分划分或人体语义分割。在本文中，我们提出了一种称为MDRS（带有区域分割的多尺度判别网络）的方法，该方法将多尺度判别特征学习，水平条带分割和语义分割集成在一个框架中，其中多尺度水平条带分割和使用全局和局部特征的组合使得该框架对于人体姿势变化，遮挡和背景混乱具有鲁棒性，并且语义分割通过共享的多尺度特征提取提高了人员识别的性能。MDRS经过端到端的多任务学习策略培训，该策略同时考虑了三个任务：人员识别，三元组预测和逐像素语义分割。全面的实验证实，我们的方法超越了许多方法，并且在Market-1501，DukeMTMC-reid和CUHK03等主流评估数据集上稳健地取得了出色的性能。