A Global-Local Architecture Constrained by Multiple Attributes for Person Re-identification

Person re-identification (person re-ID) is often considered as a sub-problem of image retrieval, which aims to match pedestrians under non-overlapping cameras. In this work, we present a novel global and local network structure integrating pedestrian identities with multiple attributes to improve the performance of person re-ID. The proposed framework consists of three modules: shared one, global one and local one. The shared module based on pre-trained residual network extracts low-level and mid-level features. And the global module guided by identification loss learns high-level semantic feature representations. To achieve accurate localization of local attribute features, we propose a multi-attributes partitioning learning method and consider pedestrian attributes as supervised information of the local module. Meanwhile, we employ whole-to-part spatial transformer networks (STNs) to achieve coarse-to-fine meaningful feature locations. By applying a multi-task learning strategy, we design various objective functions including identification and multiple attributes classification losses for training our model. The experimental results on several challenging datasets show our method significantly improves person re-ID performance and surpasses most of the state-of-the-art methods. Specifically, our model achieves 87.49% of the attribute recognition accuracy on Market1501 dataset.