Improved Two-Branch Person Re-identification Algorithm Based on Transformer

doi:10.12068/j.issn.1005-3026.2023.01.004

Abstract

Abstract: In order to solve the problem of insufficient global information modeling of person re-recognition algorithm based on convolutional neural network， the limitation of convolution operation is analyzed， and an improved global-local two-branch person re-recognition algorithm based on Transformer is proposed. Firstly， the multi-headed self-attention mechanism which is embedded in the Resnet50 backbone network is optimized by relative position-coding. After that， the processed image is split into two parts on the global branch geometrically， and the ability of extracting the abstract features is enhanced by the Transformer’s global receptive field. On the local branch， the Layer_3 output is under the supervision of dimensionality reduction while the multi-scale pooling obtains richer local features. The experimental result shows that， on the Market-1501 and DukeMTMC-reID datasets， mAP/Rank-1 of the algorithm reaches 93.45%/95.61% and 88.79%/90.35%， respectively. Compared with the algorithm which is only based on convolutional neural network， higher accuracy is achieved.

Key words: person re-identification; Transformer; convolutional neural network; feature extraction; multi-headed self-attention mechanism

CLC Number:

TP181

LIU Yang， YAN Dong-mei， MENG Fan-wei. Improved Two-Branch Person Re-identification Algorithm Based on Transformer[J]. Journal of Northeastern University(Natural Science), 2023, 44(1): 26-32.

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