Abstract：In order to solve the problem of poor retrieval accuracy due to the lack of sufficient and recognizable feature information extracted by the existing pedestrian re-identification methods, a pedestrian re-identification method based on the asymmetric enhanced attention and feature cross fusion is proposed. First, an asymmetric enhanced attention module is constructed, and the salient feature representation is enhanced through the cross-neighbor channel interactive attention that is aggregated by multiple pooling, so that the pedestrian area in the image is focused by the network. Then, taking into account the differences and relevance between the features of each layer of the network, a feature cross fusion module is constructed, and the cross fusion method is used to achieve cross-level fusion of features at different levels in the same layer, thereby realizing multi-scale fusion. Finally, the output features are segmented horizontally to obtain local features, so as to describe pedestrians in a specific area. The proposed method is verified on the three public data sets of Market1501, DukeMTMC-reID and CUHK03. Rank-1 reach 93.5%, 85.1% and 64.3% respectively, proving that the method is superior in improving the performance of pedestrian re-identification.
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2022, Vol. 43 
