Multi-Scale Occluded Person Re-Identification Guided by Key Fine-Grained Information
To reduce the influence of background and occlusion on the accuracy of pedestrian identity Re-IDentification(ReID)and make full use of the complementarity between fine-grained and coarse-grained information,a multi-scale occluded pedestrian ReID network guided by key fine-grained information is proposed.First,the image is divided into two types of overlapping patches with different sizes to better simulate the multi-scale characteristics of human observing images and the continuity characteristics of human observing adjacent regions,so a multi-scale recognition network containing both fine-grained and coarse-grained information extraction branches is constructed.Then,considering fine-grained information contains more details and there are similarities and differences between fine-grained and coarse-grained information,fine-grained attention module is further employed to realize the guide of the fine-grained branch to the coarse-grained branch.Among them,the fine-grained information is the key information retained after filtering out the interference information by the Interference Information Elimination(IIE)module.Finally,the key information related to pedestrian ReID is obtained by bivariate difference,and the prediction of pedestrian identity is realized by multi-dimensional joint supervision such as tags and features.Extensive experiments on several public pedestrian ReID databases prove the superiority of this algorithm and the effectiveness and necessity of each module.
Occluded person ReIDMulti-scaleFine-grained informationCoarse-grained informationInterference information elimination
NETL
NSTL
万方数据
