Person Re-identification Based on Pose Estimation and Transformer Model
Person re-identification(ReID)is a technology that utilizes artificial intelligence to solve public safety application problems such as border inspection and personnel tracking.It has the ability to identify a specific person from images collected across devices.However,in person tracking and other issues,deliberate person occlusion and complex scene environment occlusion greatly increases the difficulty of person re-identification.An improved person re-identification network PT-Net based on ResNet50 network was proposed,which combined with pose estimation and Transformer models to improve the person re-identification ability under occlusion conditions.The existing pose estimation method was utilized to detect key-points in the input image,and combined the key-point information with the person feature maps to generate a pose based person feature representation.Then,the Transformer model was used to encode the pose-based person feature representation for feature alignment and fusion.Based on the internationally available dataset Occluded-Duke,the experimental validation was conducted.The results show that the PT-Net method improves its mean average precision(mAP)and similarity ranking Rank-1 increase by 1.3,1.5 percentage points compared to the baseline model,respectively,verifying the effectiveness and superiority of the method.
person re-identification(ReID)pose estimationTransformer modelocclusionkey-point detection
万方数据
维普
