Person Re-identification Based on Stepped Feature Space Segmentation and Local Attention Mechanism

SHI Yuexiang; ZHOU Yue

doi:10.11999/JEIT201006

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 195-202

SHI Yuexiang, ZHOU Yue. Person Re-identification Based on Stepped Feature Space Segmentation and Local Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(1): 195-202. doi: 10.11999/JEIT201006

Citation:

SHI Yuexiang, ZHOU Yue. Person Re-identification Based on Stepped Feature Space Segmentation and Local Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(1): 195-202. doi: 10.11999/JEIT201006

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Person Re-identification Based on Stepped Feature Space Segmentation and Local Attention Mechanism

doi: 10.11999/JEIT201006

SHI Yuexiang,
ZHOU Yue^,

School of Computer Science and School of Cyberspace Security, Xiangtan University, Xiangtan 411105, China

Funds: The National Natural Science Foundation of China (61602397, 61502407)

Received Date: 2020-11-30
Accepted Date: 2021-11-05
Rev Recd Date: 2021-10-21

Available Online: 2021-11-16

Publish Date: 2022-01-10

Abstract

Abstract

In order to make the network capture more effective content distinguish pedestrians, this paper proposes a multi-branch network based on Stepped feature space segmentation and Local Branch Attention Network (SLANet) mechanism to pay attention to the salient information of local images. First of all, a stepped branch attention module is introduced into the network. This module blocks the feature map horizontally in a stepped manner, and branch attention is used to assign different weights to each branch. Secondly, a multi-scale adaptive attention module is introduced into the network, which processes local features and adapts the size of the receptive field to adapt to images of different scales. Meanwhile, channel attention and spatial attention are combined to screen out the important features of the image. In the design of network, the multi-granularity network is used to combine the global feature with the local feature. Finally, the method is validated on three widely used person re-identification data sets Market-1501, DukeMTMC-reID and CUHK03. Among them, mAP and Rank-1 on market-1501 data set reach 88.1% and 95.6% respectively. The experimental results show that the proposed network model can improve the accuracy of person re-identification.
- Person re-identification,
- Feature space segmentation,
- Attention mechanism,
- Local features

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References(24)

References

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