Beyond triplet loss: a deep quadruplet network for person re-identification

Weihua Chen, Xiaotang Chen, Jianguo Zhang (Lead / Corresponding author), Kaiqi Huang

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

293 Citations (Scopus)
143 Downloads (Pure)

Abstract

Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.
Original languageEnglish
Title of host publicationProceedings of IEEE International Conference on Computer Vision and Pattern Recognition
PublisherIEEE
Pages1320-1329
Number of pages10
ISBN (Electronic)9781538604571
ISBN (Print)9781538604588
DOIs
Publication statusE-pub ahead of print - 9 Nov 2017
EventIEEE International Conference on Computer Vision and Pattern Recognition - Hawaii Convention Center, Honolulu, United States
Duration: 21 Jul 201726 Jul 2017
http://cvpr2017.thecvf.com/

Conference

ConferenceIEEE International Conference on Computer Vision and Pattern Recognition
CountryUnited States
CityHonolulu
Period21/07/1726/07/17
Internet address

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Keywords

  • Probes
  • Training
  • Testing
  • Machine learning
  • Feature extraction
  • Loss measurement

Cite this

Chen, W., Chen, X., Zhang, J., & Huang, K. (2017). Beyond triplet loss: a deep quadruplet network for person re-identification . In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (pp. 1320-1329). IEEE. https://doi.org/10.1109/CVPR.2017.145