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).

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
[1]SONG W R,ZHAO Q Q,CHEN C H,et al.Survey on pedes-trian re-identification research[J].CAAI Transactions on Intelligent Systems,2017,12(6):770-780.
[2]WU Z,LI Y,RADKE R J.Viewpoint invariant human re-identification in camera networks using pose priors and subject-discriminative features[J].IEEE Transactions on Pattern Analysis &Machine Intelligence,2015,37(5):1095-1108.
[3]WANG J,WANG Z,LIANG C,et al.Equidistance constrained metric learning for person re-identification[J].Pattern Recognition,2018,74(Feb.):38-51.
[4]YANG F,XU Y,YIN M X,et al.Review on deeplearning-based pedestrian re-identification[J].Journal of Computer Applications,2020,40(5):1243-1252.
[5]RUI Z,OUYANG W,WANG X.Unsupervised salience lear-ning for person re-identification[C]//Proceeding of IEEE Confe-rence Computer Vision Pattern Recognition.2013:3586-3593.
[6]LIU J.Research on human weight recognition technology based on local features[D].Beijing:Beijing Jiaotong University,2019.
[7]LIU H.Research on pedestrian re inspection technology forvideo surveillance[D].Beijing:University of Chinese Academy of Sciences,2014.
[8]MARTINEL N.Accelerated low-rank sparse metric learning for person re-identification[J].Pattern Recognition Letters,2018,112(Sep.1):234-240.
[9]GÜLER R A,NEVEROVA N,KOKKINOS I.DensePose:Dense Human Pose Estimation In The Wild[C]//Conference on Computer Vision and Pattern Recognition(CVPR).2018:7297-7306.
[10]XIONG W,FENG C,XIONG Z J,et al.Improved pedestrianrecognition technology based on CNN[J].Computer Enginee-ring and Science,2019,41(4):95-102.
[11]DAI C C,WANG H Y,NI T G,et al.Person re-identification based on deep convolutional generative adversarial network and expanded neighbor reranking[J].Journal of Computer Research and Development,2019,56(8):1632-1641.
[12]LEI Z,YANG K,JIANG K,et al.KDAS-ReID:Architecturesearch for person re-identification via distilled knowledge with dynamic temperature[J].Algorithms,2021,14(5):137-148.
[13]YAN Y,NI B,LIU J,et al.Multi-level attention model for person re-identification[J].Pattern Recognition Letters,2018,127(4):156-164.
[14]LI C,ZHAO S L,ZHAO J P,et al.Scaling-up algorithm of multi-scale association rules[J].Computer Science,2017,44(8):285-289.
[15]CHEN C,QI F.Review on development of convolutionalneural network and its application in computer vision[J].Computer Science,2019,46(3):69-79.
[16]MAHENDRAN A,VEDALDI A.Visualizing deep convolutional neural networks using natural pre-images[J].International Journal of Computer Vision,2015,120(3):75-83.
[17]SONG G,LENG B,YU L,et al.Region-based quality estimation network for large-scale person re-identification[J].arXiv:1711.08766v2,2017.
[18]LU D,MA W Q.Gesture recognition based on improvedYOLOv4 tiny algorithm[J].Journal of Electronics & Information Technology,2021,43(6):1-9.
[19]GUO Y,CHEUNG N M.Efficient and deep person re-identification using multi-level similarity[C]//Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2018:2335-2344.
[20]CHANG H,QU D,WANG K,et al.Attribute-guided attention and dependency learning for improving person re-identification based on data analysis technology[J].Enterprise Information Systems,2021,47(5):1-26.
[21]RISTANI E,SOLERA F,ZOU R S,et al.Performance measuresand a data set for multi-target,multi-camera tracking[C]//European Conference on Computer Vision.Cham:Springer,2016:17-35.
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