Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (7): 142-147.doi: 10.11896/jsjkx.210600198

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism

MENG Yue-bo1,2, MU Si-rong1, LIU Guang-hui1, XU Sheng-jun1,2, HAN Jiu-qiang1,2   

  1. 1 School of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an,710055,China
    2 Artificial Intelligence and Digital Economy Guangdong Provincial Laboratory,South China University of Technology,Guangzhou 510000,China
  • Received:2021-06-24 Revised:2021-12-26 Online:2022-07-15 Published:2022-07-12
  • About author:MENG Yue-bo,born in 1979,Ph.D,associate professor.Her main research interests include computer vision perception and understanding,intelligent architecture and artificial intelligence.
    MU Si-rong,born in 1995,postgra-duate.Her main research interests include visual processing,artificial intelligence,and image processing.
  • Supported by:
    National Natural Science Foundation of China(51678470),Nature Science Foundation of Shaanxi,China(2020JM-473,2020JM-472),Natural Science Basic Research of Xi'an University of Architecture and Technology(JC1703) and Natural Science Foundation of Xi'an University of Architecture and Technology(ZR19046).

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Abstract: In order to improve the accuracy and applicability of person re-identification(Re-ID),a Re-ID method based on vector attention mechanism GoogLeNet is proposed.Firstly,three groups of images(anchor,positive and negative) are input into the GoogLeNet-GMP network to obtain segmented feature vectors.Then,spatial pyramid pooling(SPP) is used to aggregate the features from different pyramid levels,and attention mechanism is introduced.By integrating the multi-scale pooling regions which represent the visual information of the target,the distinguishable features on multiple semantic levels are obtained.At the same time,the mixed form of two different loss functions is taken as the final loss function.Experiments on Market-15012 and Duke-MTMC3 data set show that the proposed method performs better in Rank-1 and mAP indicators than other excellent methods.

Key words: Attention mechanism, GoogLeNet, Loss function, Person re-identification, Spatial pyramid pooling

CLC Number: 

  • TP391
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