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Multimedia Tools and Applications

, Volume 78, Issue 1, pp 337–362 | Cite as

Low illumination person re-identification

  • Fei Ma
  • Xiaoke Zhu
  • Xinyu Zhang
  • Liang Yang
  • Mei Zuo
  • Xiao-Yuan JingEmail author
Article
  • 127 Downloads

Abstract

Low illumination is a common problem for recognition and tracking. Low illumination video-based person re identification (re-id) is an important application in practice. Low illumination usually results in severe loss of visual appearance and space-time information contained in pedestrian image or video, which brings large difficulty to re-identification. However, the problem of low illumination video-based person re-id (LIVPR) has not been well studied. In this paper, we propose a novel triplet-based manifold discriminative distance learning (TMD2L) approach for LIVPR. By regarding each video as an image set, TMD2L aims to learn a manifold-based distance metric, under which the intrinsic structure of image sets can be preserved, and the distance between truly matching sets is smaller than that between wrong matching sets. Experiment results on the new collected low illumination person sequence (LIPS) dataset, as well as two simulated datasets LI-PRID 2011 and LI-iLIDS-VID show that our proposed approach TMD2L outperforms existing representative person re-id methods.

Keywords

Low illumination Person re-identification Local linear model Discriminative distance learning 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Fei Ma
    • 1
  • Xiaoke Zhu
    • 2
  • Xinyu Zhang
    • 1
  • Liang Yang
    • 1
  • Mei Zuo
    • 1
  • Xiao-Yuan Jing
    • 1
    Email author
  1. 1.School of Computer ScienceWuhan UniversityWuhanChina
  2. 2.School of Computer and Information EngineeringHenan UniversityKaifengChina

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