An ensemble color model for human re-identification

Xiaokai Liu, Hongyu Wang, Yi Wu, Jimei Yang, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

50 Citations (Scopus)

Abstract

Appearance-based human re-identification is challenging due to different camera characteristics, varying lighting conditions, pose variations across camera views, etc. Recent studies have revealed that color information plays a critical role on performance. However, two problems remain unclear: (1) how do different color descriptors perform under the same scene in re-identification problem? and (2) how can we combine these descriptors without losing their invariance property and distinctiveness power? In this paper, we propose a novel ensemble model that combines different color descriptors in the decision level through metric learning. Experiments show that the proposed system significantly outperforms state-of-the-art algorithms on two challenging datasets (VIPeR and PRID 450S). We have improved the Rank 1 recognition rate on VIPeR dataset by 8.7%.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages868-875
Number of pages8
ISBN (Electronic)9781479966820
DOIs
Publication statusPublished - 2015 Feb 19
Event2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 - Waikoloa, United States
Duration: 2015 Jan 52015 Jan 9

Publication series

NameProceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015

Conference

Conference2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015
Country/TerritoryUnited States
CityWaikoloa
Period15/1/515/1/9

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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