Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (10): 204-211.doi: 10.11896/jsjkx.210300128

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Multi-orientation Partitioned Network for Person Re-identification

TANG Yi-xing, LIU Xue-liang, HU She-jiao   

  1. School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230031,China
  • Received:2021-03-12 Revised:2021-05-26 Online:2021-10-15 Published:2021-10-18
  • About author:TANG Yi-xing,born in 1995,postgra-duate.Her main research interests include computer vision and so on.
    LIU Xue-liang,born in 1981,Ph.D,professor,is a member of China Computer Federation.His main research interests include computer vision and multimedia information retrieval.
  • Supported by:
    National Key R&D Program of China(2018AAA0102002) and National Natural Science Foundation of China(61932009,61632007,61976076).

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Abstract: Combining global features with local features is an important solution to improve discriminative performances in person re-identification (Re-ID) task.In the past,external information was used to locate regions with corresponding semantics,thus mining local information.Most of these methods are not end-to-end,so the training process is complex.To solve this problem,a multi-orientation partitioned network (MOPN) is proposed,which can effectively mine local information and combine global information with local information for end-to-end feature learning.The network has three branches:one for extracting global feature and two for mining local information.Without relying on external information,the algorithm divides pedestrians' images into hori-zontal and vertical stripes in different local branches respectively,so as to obtain different local feature representations.Plenty of experiments conducted on Market-1501,DukeMTMC-reID,CUHK03 and cross-modal dataset SketchRe-ID show that the proposed method has better overall performance than other comparison algorithms,and is effective and robust.

Key words: Deep learning, Global feature, Local feature, Multi-branch network, Person Re-identification

CLC Number: 

  • TP391
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