Person Re-Identification Method Based on Region Feature Completion and Fine-Grained Feature Attention
To address the issues of low utilization of occluded region features and insufficient granularity of extracted fea-tures in existing person re-identification methods,a person re-identification method based on region feature completion and fine-grained feature attention is proposed in this article.Firstly,a fine-grained feature attention module is proposed to seg-ment,weight,and recombine the input features,with the addition of attention mechanisms to obtain fine-grained features.Secondly,a region feature completion module is introduced,which clusters the input features into blocks to enable the re-covery of occluded region features through features in the same cluster.Finally,the model is optimized with the use of iden-tity loss,weighted regularized triplet loss,and center loss.Experimental results on publicly available datasets Market-1501 and DukeMTMC-reID demonstrated that the proposed model achieved improved recognition performance.
person re-identificationregion feature completionfine-grainedattention mechanism
万方数据
维普
