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Combine Coarse and Fine Cues: Multi-grained Fusion Network for Video-Based Person Re-identification

  • Chao Li
  • Lei Liu
  • Kai Lv
  • Hao Sheng
  • Wei Ke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

Video-based person re-identification aims to precisely match video sequences of pedestrian across non-overlapped cameras. Existing methods deal with this task by encoding each frame and aggregating them along time. In order to increase the discriminative ability of video features, we propose an end-to-end framework called Multi-grained Fusion Network (MGFN) which aims to keep both global and local information by combining frame-level representations with different granularities. The final video features are generated by aggregating multi-grained representations on both spatial and temporal. Experiments indicate our method achieves excellent performance on three widely used datasets named PRID-2011, iLIDS-VID, and MARS. Especially on MARS, MGFN surpass state-of-the-art result by \(11.5\%\).

Keywords

Video-based person re-identification Multi-grained fusion network Part-based model Multi-grained feature 

Notes

Acknowledgement

This study is partially supported by the National Key R&D Program of China (No. 2017YFB1002000 ), the National Natural Science Foundation of China (No. 61472019), the Macao Science and Technology Development Fund (No. 138/2016/A3), the Program of Introducing Talents of Discipline to Universities and the Open Fund of the State Key Laboratory of Software Development Environment under grant SKLSDE-2017ZX-09, the Project of Experimental Verification of the Basic Commonness and Key Technical Standards of the Industrial Internet network architecture. Thank you for the support from HAWKEYE Group.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Software Development Environment, School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.Shenzhen Key Laboratory of Data Vitalization, Research Institute in ShenzhenBeihang UniversityShenzhenPeople’s Republic of China
  3. 3.Macao Polytechnic InstituteMacaoPeople’s Republic of China

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