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

Yan-Zhen Zhong; Wen-Ze Shao; Qi Ge; Li-Qian Wang; Shi-Peng Xie; Juan Xu; Hai-Bo Li

doi:10.1117/12.2539984

14 August 2019 Joint end-to-end learning for scale-adaptive person super-resolution and re-identification

Yan-Zhen Zhong, Wen-Ze Shao, Qi Ge, Li-Qian Wang, Shi-Peng Xie, Juan Xu, Hai-Bo Li

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111791U (2019) https://doi.org/10.1117/12.2539984
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

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.

Citation Download Citation

Yan-Zhen Zhong, Wen-Ze Shao, Qi Ge, Li-Qian Wang, Shi-Peng Xie, Juan Xu, and Hai-Bo Li "Joint end-to-end learning for scale-adaptive person super-resolution and re-identification", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791U (14 August 2019); https://doi.org/10.1117/12.2539984

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KEYWORDS

Super resolution

Lawrencium

Cameras

Data modeling

Performance modeling

Image resolution

Binary data

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