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Spatial Quality Aware Network for Video-Based Person Re-identification

  • Yujie Wang
  • , Biao Leng*
  • , Guanglu Song
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Person re-identification in video is challenging in computer vision. Most methods adopt feature aggregation to get a video-level representation. However, almost all of them do it on the final feature embedding, which neglects the spatial difference among feature maps. To address this problem, we proposed an effective approach, named Spatial Quality Aware Network (SQAN) for video-based person re-identification. SQAN distributes a score for each pixel in a feature map. Then scores are normalized across all frames and the weighted sum is used to aggregate them. To deal with overfitting, we also proposed a semantic dropout strategy. Experiments show that our proposed method is competitive with state-of-the-art methods in performance.

Original languageEnglish
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
EditorsDerong Liu, Shengli Xie, El-Sayed M. El-Alfy, Dongbin Zhao, Yuanqing Li
PublisherSpringer Verlag
Pages34-43
Number of pages10
ISBN (Print)9783319700892
DOIs
StatePublished - 2017
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: 14 Nov 201718 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10636 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Neural Information Processing, ICONIP 2017
Country/TerritoryChina
CityGuangzhou
Period14/11/1718/11/17

Keywords

  • Deep learning
  • Feature aggregation
  • Person re-identification

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