Person Re-Identification by Deep Learning Muti-Part Information Complementary
Xiao Hu, Zhuqing Jiang, Xiaoqiang Guo, Yun Zhou · 2018
Person re-identification (Re-ID) aims to identify people across disjoint camera views, which is considered either a binary classification task or a ranking task. However, the importance of feature extracting in both tasks is the same. In this paper, we introduce Global-Part Network (GPN) which employ multi-part information fusion with Feature Weighting Structure (FWS) to address the person Re-ID as a retrieval task. In our framework, the global and body-part features of a specific person can be complementary to each other to enhance the final feature representation. Furthermore, we utilize a state-of-the-art re-ranking method to improve the ranking performance. Extensive experimental analyses and results on three popular datasets, i.e., Market1501, CUHK03, CUHK01, demonstrate the effectiveness of the proposed approach.