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Proceedings Paper

Joint end-to-end learning for scale-adaptive person super-resolution and re-identification
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Paper Abstract

Persons captured in real-life scenarios are generally in non-uniform scales. However, most generally acknowledged person re-identification (Re-ID) methods lay emphasis on matching normal-scale high-resolution person images. To address this problem, the ideas of existing image reconstruction techniques are incorporated which are expected contribute to recover accurate appearance information for low-resolution person Re-ID. In specific, this paper proposes a joint deep learning approach for Scale-Adaptive person Super-Resolution and Re-identification (SASR2 ). It is for the first time that scale-adaptive learning is jointly implemented for super-resolution and re-identification without any extra post-processing process. With the super-resolution module, the high-resolution appearance information can be automatically reconstructed from scales of low-resolution person images, bringing a direct beneficial impact on the subsequent Re-ID thanks to the joint learning nature of the proposed approach. It deserves noting that SASR2 is not only simple but also flexible, since it can be adaptable to person Re-ID on both multi-scale LR and normal-scale HR datasets. A large amount of experimental analysis demonstrates that SASR2 achieves competitive performance compared with previous low-resolution Re-ID methods especially on the realistic CAVIAR dataset.

Paper Details

Date Published: 14 August 2019
PDF: 9 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791U (14 August 2019); doi: 10.1117/12.2539984
Show Author Affiliations
Yan-Zhen Zhong, Nanjing Univ. of Posts and Telecommunications (China)
Wen-Ze Shao, Nanjing Univ. of Posts and Telecommunications (China)
Qi Ge, Nanjing Univ. of Posts and Telecommunications (China)
Li-Qian Wang, Nanjing Univ. of Posts and Telecommunications (China)
Shi-Peng Xie, Nanjing Univ. of Posts and Telecommunications (China)
Juan Xu, Nanjing Univ. of Posts and Telecommunications (China)
Hai-Bo Li, Nanjing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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