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Random linear interpolation data augmentation for person re-identification

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Abstract

Person Re-Identification (person re-ID) is an image retrieval task which identifies the same person in different camera views. Generally, a good person re-ID model requires a large dataset containing over 100000 images to reduce the risk of over-fitting. Most current handcrafted person re-ID datasets, however, are insufficient for training a learning model with high generalization ability. In addition, the lacking of images with various levels of occlusion is still remaining in most existing datasets. Motivated by these two problems, this paper proposes a new data augmentation method called Random Linear Interpolation that can enlarge the sizes of person re-ID datasets and improve the generalization ability of the learning model. The key enabler of our approach is generating fused images by interpolating pairs of original images. In other words, the innovation of the proposed approach is considering data augmentation between two random samples. Plenty of experimental results demonstrates that the proposed method is effective to improve baseline models. On Market1501 and DukeMTMC-reID datasets, our approach can achieve 92.71% and 82.19% rank-1 accuracy, respectively.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Program No. 61702415, No.61502387), Natural Science Basic Research Plan in Shaanxi Province of China (Program No.2017JM6056,2016JQ6029) and Talent Support Project of Science Association in Shaanxi Province (Program No. 20180108).

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Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xian, China

    Zhi Li, Jun Guo, Wenli Jiao, Pengfei Xu, Baoying Liu & Xiaowei Zhao

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  1. Zhi Li
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  2. Jun Guo
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  3. Wenli Jiao
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  4. Pengfei Xu
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  5. Baoying Liu
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  6. Xiaowei Zhao
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Correspondence to Pengfei Xu.

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Li, Z., Guo, J., Jiao, W. et al. Random linear interpolation data augmentation for person re-identification. Multimed Tools Appl 79, 4931–4947 (2020). https://doi.org/10.1007/s11042-018-7071-5

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