Computer Science ›› 2021, Vol. 48 ›› Issue (7): 238-244.doi: 10.11896/jsjkx.200600043

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Multi-scale Multi-granularity Feature for Pedestrian Re-identification

WANG Dong1, ZHOU Da-ke1,2, HUANG You-da1 , YANG Xin1   

  1. 1 School of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211100,China
    2 Jiangsu Key Laboratory of Internet of Things and Control Technologies (Nanjing University of Aeronautics and Astronautics),Nanjing 211100,China
  • Received:2020-06-05 Revised:2020-09-18 Online:2021-07-15 Published:2021-07-02
  • About author:WANG Dong,born in 1996,postgra-duate.His main research interests include target detection,pedestrian re-identification and target tracking.(
    ZHOU Da-ke,born in 1974,Ph.D,associate professor.His main research in-terests include digital image processing,computer vision and machine learning.
  • Supported by:
    National Natural Science Foundation of China(61573182).

Abstract: In order to address the problem of insufficient discriminative features for pedestrian re-identification extracted by exis-ting convolutional neural network,a novel multi-scale multi-granularity feature learning for pedestrian re-identification method is proposed.In the training phase,the method extracts multi-scale features at different stages of the convolutional neural network,and then blocks and pools these feature maps to obtain multi-granularity features containing global and local features,uses uncertainty to weight Softmax loss and triples loss and to supervise training process on feature vectors.In the inference phase,the obtained multi-scale multi-granularity features are concatenated,and finally the concatenated features are used to perform similarity matching in the gallery.Experiments on the Market-1501 and DukeMTMC-ReID datasets show that the proposed method improves the Rank-1 evaluation index by 4.3% and 3.6%,respectively,compared with the benchmark network ResNet-50,and improves the mAP evaluation index respectively 6.2% and 6.6%.The results show that the proposed method can enhance the discrimination of extracted features and improve the performance of pedestrian re-identification.

Key words: Convolutional neural network, Machine vision, Multi-granularity features, Multi-scale features, Pedestrian re-identification

CLC Number: 

  • TP399
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