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Camera Style Guided Feature Generation for Person Re-identification

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Wireless Algorithms, Systems, and Applications (WASA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12384))

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Abstract

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.

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Acknowledgement

This study is partially supported by the National Key R&D Program of China (No. 2019YFB2101600), the National Natural Science Foundation of China (No. 61861166002, 61872025, 61635002) , the Science and Technology Development Fund, Macau SAR (File no. 0001/2018/AFJ), the Fundamental Research Funds for the Central Universities and the Open Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE2019ZX-04). Thank you for the support from HAWKEYE Group.

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Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, 100191, People’s Republic of China

    Hantao Hu, Yang Liu, Kai Lv & Hao Sheng

  2. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, People’s Republic of China

    Hao Sheng

  3. Beihang Hangzhou Institute for Innovation at Yuhang, Beihang University, Hangzhou, 311121, People’s Republic of China

    Hantao Hu, Yang Liu, Kai Lv & Hao Sheng

  4. School of Computer Science and Technology, Shandong University, Qingdao, 266237, People’s Republic of China

    Yanwei Zheng

  5. School of Applied Sciences, Macao Polytechnic Institute, Macau, People’s Republic of China

    Wei Zhang & Wei Ke

Authors
  1. Hantao Hu
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  2. Yang Liu
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  3. Kai Lv
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  4. Yanwei Zheng
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  5. Wei Zhang
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  6. Wei Ke
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  7. Hao Sheng
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Corresponding author

Correspondence to Hao Sheng .

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Editors and Affiliations

  1. Shandong University, Qingdao, China

    Dongxiao Yu

  2. TU Berlin, Berlin, Germany

    Falko Dressler

  3. Qilu University of Technology, Jinan, China

    Jiguo Yu

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Hu, H. et al. (2020). Camera Style Guided Feature Generation for Person Re-identification. In: Yu, D., Dressler, F., Yu, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2020. Lecture Notes in Computer Science(), vol 12384. Springer, Cham. https://doi.org/10.1007/978-3-030-59016-1_14

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  • DOI: https://doi.org/10.1007/978-3-030-59016-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59015-4

  • Online ISBN: 978-3-030-59016-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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