Multi-level and multi-scale horizontal pooling network for person re-identification

Yunzhou Zhang, Shuangwei Liu, Lin Qi, Sonya Coleman, Dermot Kerr, Weidong Shi

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
214 Downloads (Pure)


Person re-identification (Re-ID) is the task of matching a target person across different cameras, which has drawn extensive attention in computer vision and has become an essential component in the video surveillance system. Despite recent remarkable progress, person re-identification methods are either subject to the power of feature representation, or give equal importance to all examples. To mitigate these issues, we introduce a simple, yet effective, Multi-level and Multi-scale Horizontal Pooling Network (MMHPN) for person re-identification. Concretely, our contributions are three-fold:1) we take partial feature representation into account at different pooling scales and different semantic levels so that various partial information is obtained to form a robust descriptor; 2) we introduce a Part Sensitive Loss (PSL) to reduce the effect of easily classified partition to facilitate training of the person re-identification network, 3) we conduct extensive experimental results using the Market-1501, DukeMTMC-reID and CUHK03 datasets and achieve mAP scores of 83.4%, 75.1% and 65.4% respectively on these challenging datasets.

Original languageEnglish
Pages (from-to)28603-28619
Number of pages17
JournalMultimedia Tools and Applications
Issue number39-40
Early online date5 Aug 2020
Publication statusPublished (in print/issue) - 31 Oct 2020

Bibliographical note

Funding Information:
This work is supported by National Natural Science Foundation of China (No. 61973066, 61471110), Foundation Project of National Key Laboratory (6142002301, 61420030302), the Distinguished Creative Talent Program of Shenyang(RC170490) and the Fundamental Research Funds for the Central Universities (N172608005, N182608004).

Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Copyright 2020 Elsevier B.V., All rights reserved.


  • Horizontal pooling network
  • Multi-level and multi-scale
  • Part sensitive loss
  • Person re-identification


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