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Volume 49 Issue 12
Dec.  2023
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Journal of Beijing University of Aeronautics and Astronautics  > 2023  >  49(12): 3511-3519.
ZHANG N，CHENG D Q，KOU Q Q，et al. Person re-identification based on random occlusion and multi-granularity feature fusion[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（12）：3511-3519 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0091
Citation: ZHANG N，CHENG D Q，KOU Q Q，et al. Person re-identification based on random occlusion and multi-granularity feature fusion[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（12）：3511-3519 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0091
shu

Person re-identification based on random occlusion and multi-granularity feature fusion

doi: 10.13700/j.bh.1001-5965.2022.0091
  • 1.

    School of Information and Control Engineering，China University of Mining and Technology，Xuzhou 221116，China

  • 2.

    School of Computer Science and Technology，China University of Mining and Technology，Xuzhou 221116，China

Funds:

National Natural Science Foundation of China (51774281) 

More Information
  • Corresponding author: E-mail：chengdq@cumt.edu.cn
  • Received Date: 28 Feb 2022
  • Accepted Date: 25 Mar 2022
  • Publish Date: 11 Apr 2022
    Abstract

  • Aiming at the problems of occlusion and monotony of pedestrian discriminative feature hierarchy in person re-identification, this paper proposes a method combining random occlusion and multi-granularity feature fusion based on the IBN-Net50-a network. First, in order to enhance the robustness against occlusion, random occlusion processing is performed on the input images to simulate the real scene of pedestrians being occluded. Secondly, the network includes a global branch, a local coarse-grained fusion branch and a local fine-grained fusion branch, which can extract global salient features while supplementing local multi-grained deep features, enriching the hierarchy of pedestrian discrimination features. Furthermore, further mining the correlation between local multi-granularity features for deeper fusion. Finally, the label smoothing loss and triplet loss jointly train the network. Comparing the proposed method with current state-of-the-art person re-identification algorithms on three standard public datasets and one occlusion dataset. The experimental results show that the Rank-1 of the proposed algorithm on Market1501, DukeMTMC-reID and CUHK03 is 95.2%, 89.2% and 80.1%, respectively. In Occluded-Duke dataset, Rank-1 and mAP achieved 60.6% and 51.6%. The experimental results are better than those of the compared methods, which fully confirm the effectiveness of the proposed method.

     

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