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Person Re-identification with pose variation aware data augmentation

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Abstract

Person re-identification (Re-ID) aims to match a person of interest across multiple non-overlapping camera views. This is a challenging task, partly because a person captured in surveillance video often undergoes intense pose variations. Consequently, differences in their appearance are typically obvious. In this paper, we propose a pose variation aware data augmentation (\(\hbox {PA}^4\)) method, which is composed of a pose transfer generative adversarial network (PTGAN) and person re-identification with improved hard example mining (Pre-HEM). Specifically, PTGAN introduces a similarity measurement module to synthesize realistic person images that are conditional on the pose, and with the original images, form an augmented training dataset. Pre-HEM presents a novel method of using the pose-transferred images with the learned pose transfer model for person Re-ID. It replaces the invalid samples that are caused by pose variations and constrains the proportion of the pose-transferred samples in each mini-batch. We conduct extensive comparative evaluations to demonstrate the advantages and superiority of our proposed method over state-of-the-art approaches on Market-1501, DukeMTMC-reID, and CUHK03 dataset.

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Acknowledgements

This work was supported by the National Key R&D Program of China (Grant No. 2018YFB2100603) and the National Natural Science Foundation of China (Grant No. 61872024). The authors would like to thank the anonymous reviewers for their constructive comments and suggestions.

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Authors and Affiliations

  1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, XueYuan Road No. 37, HaiDian District, Beijing, 100191, China

    Lei Zhang, Qishuai Diao, Zhong Zhou & Wei Wu

  2. Information Engineering College, Capital Normal University, North Xisanhuan Road No. 105, Beijing, 100089, China

    Na Jiang

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  1. Lei Zhang
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Correspondence to Zhong Zhou.

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Zhang, L., Jiang, N., Diao, Q. et al. Person Re-identification with pose variation aware data augmentation. Neural Comput & Applic 34, 11817–11830 (2022). https://doi.org/10.1007/s00521-022-07071-1

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