Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism - MENG Yue-bo, MU Si-rong, LIU Guang-hui, XU Sheng-jun, HAN Jiu-qiang (Jisuanji kexue, 2022)
In order to improve the accuracy and applicability of person re-identification(Re-ID),a Re-ID method based on vector attention mechanism GoogLeNet is proposed.Firstly,three groups of images(anchor,positive and negative) are input into the GoogLeNet-GMP network to obtain segmented feature vectors.Then,spatial pyramid pooling(SPP) is used to aggregate the features from different pyramid levels,and attention mechanism is introduced.By integrating the multi-scale pooling regions which represent the visual information of the target,the distinguishable features on multiple semantic levels are obtained.At the same time,the mixed form of two different loss functions is taken as the final loss function.Experiments on Market-15012 and Duke-MTMC3 data set show that the proposed method performs better in Rank-1 and mAP indicators than other excellent methods.