Person Re-identification Algorithm Based on Spatial Attention Network.

Person Re-identification (Re-ID) aims to solve the matching problem of the same pedestrian at a different time and in different places. Due to the cross-device condition, the appearance of different pedestrians may have a high degree of similarity, at this time, using the global features of pedestrians to match often cannot achieve good results. In order to solve these problems, we designed a Spatial Attention Network (SAN), which introduces attribute features as auxiliary information. Different from the previous approach of simply adding a branch of attribute binary classification network, our SAN is mainly divided into two connecting steps. First, we generate Attribute Attention Heat map (AAH) through Grad-CAM algorithm to accurately locate fine-grained attribute areas of pedestrians. Then, the Attribute Spatial Attention Module (ASAM) is constructed according to the AHH which is taken as the prior knowledge, and introduced into the Re-ID network to assist in the discrimination of the Re-ID task. In particular, our SAN network can integrate the local attribute information and global ID information of pedestrians, which has good adaptability. The test results on Market1501 and DukeMTMC-reID show that our SAN can achieve good results, which is obviously competitive compared with most Re-ID algorithms.