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Person Re-Identification Based on Attention Mechanism and Context Information Fusion

Author

Listed:
  • Shengbo Chen

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 201112, China)

  • Hongchang Zhang

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Zhou Lei

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

Abstract

Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information. Aiming at the problem of person context information loss due to the over depth of the network, a context information fusion module is designed to sample the shallow feature map of pedestrians and cascade with the high-level feature map. In order to improve the robustness, the model is trained by combining the loss of margin sample mining with the loss function of cross entropy. Experiments are carried out on datasets Market1501 and DukeMTMC-reID, our method achieves rank-1 accuracy of 95.9% on the Market1501 dataset, and 90.1% on the DukeMTMC-reID dataset, outperforming the current mainstream method in case of only using global feature.

Suggested Citation

  • Shengbo Chen & Hongchang Zhang & Zhou Lei, 2021. "Person Re-Identification Based on Attention Mechanism and Context Information Fusion," Future Internet, MDPI, vol. 13(3), pages 1-15, March.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:3:p:72-:d:516367
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    Cited by:

    1. Weiwei Zhang & Xin Ma & Yuzhao Zhang & Ming Ji & Chenghui Zhen, 2022. "SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios," Future Internet, MDPI, vol. 14(1), pages 1-14, January.

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