Camera Style Guided Feature Generation for Person Re-identification

Hantao Hu, Yang Liu, Kai Lv, Yanwei Zheng, Wei Zhang, Wei Ke, Hao Sheng

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

1 Citation (Scopus)


Camera variance has always been a troublesome matter in person re-identification (re-ID). Recently, more and more interests have grown in alleviating the camera variance problem by data augmentation through generative models. However, these methods, mostly based on image-level generative adversarial networks (GANs), require huge computational power during the training process of generative models. In this paper, we propose to solve the person re-ID problem by adopting a feature level camera-style guided GAN, which can serve as an intra-class augmentation method to enhance the model robustness against camera variance. Specifically, the proposed method makes camera-style transfer on input features while preserving the corresponding identity information. Moreover, the training process can be directly injected into the re-ID task in an end-to-end manner, which means we can deploy our methods with much less time and space costs. Experiments show the validity of the generative model and its benefits towards re-ID performance on Market-1501 and DukeMTMC-reID datasets.

Original languageEnglish
Title of host publicationWireless Algorithms, Systems, and Applications - 15th International Conference, WASA 2020, Proceedings
EditorsDongxiao Yu, Falko Dressler, Jiguo Yu
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030590154
Publication statusPublished - 2020
Event15th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2020 - Qingdao, China
Duration: 13 Sept 202015 Sept 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12384 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2020


  • Adaptive batch normalization
  • Camera-style guided
  • Feature generation
  • Generative adversarial networks
  • Person re-identification


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