基于增强特征融合网络的行人再识别 postprint

Abstract: Person re-identification is to judge whether the pedestrian across different cameras belongs to the same person or not. While it is challenging task due to the large variations in person pose, occlusion, background clutter, etc. And several deep learning based person re-identification have been proposed and achieved remarkable performance. However, these methods are only considered separately from the local or global features of the pedestrian, ignoring the relationship between the features. So this paper proposed the enhanced feature convergent network (EFCN) . In the global branch, the paper used to employ the new attention to pay close attention to the global feature of pedestrians. In the local branch, it proposed the gated recurrent unit change network(GRU-CN) to obtain more robust local features, and then this paper used feature fusion to connect the extracted global and local features. Extensive comparative experiments show that EFCN can achieve better experimental results on three standard person Re-ID datasets. The proposed enhanced feature convergent network can extract highly discriminative pedestrian features. This model can be applied to the problem of Re-ID under non-overlapping multi-cameras in large scenes. It has high recognition ability and accuracy. The method can extract robust features for pedestrian images with changing background.