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Person re-identification based on human semantic parsing and message passing

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Abstract

As an effective method to assist intelligent management of public areas, person re-identification (re-ID) technology has been developed rapidly in recent years. However, there are still some insurmountable obstacles in practical applications, among which misalignment caused by some factors is a challenging problem. Unlike previous approaches that only superficially mine pedestrian information to solve this problem, we propose an aligned person re-ID method based on human semantic parsing and message passing. Our method achieves pixel-level alignment through the incorporation of semantic parsing and also utilizes the results of semantic parsing. It constructs a graph neural network based on the structure of the human body to achieve information interaction between various part features. Additionally, various semantic features and a global feature are considered and used in the loss function for the ensemble of features, thereby ensuring the discrimination and robustness. Such ensemble learning allows our method to perform well not only for the unaligned case, but also have the ability to handle occlusion. Thus, the proposed SPMP method achieves better performance than most existing methods on multiple popular datasets.

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Data Availability Statement

All data generated or analyzed during this study are included in this published article.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61573114) and the Science and Technology on Underwater Test and Control Laboratory under Grant (YS24071804).

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Authors and Affiliations

  1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Chunyan Lyu, Tong Xu, Kejun Wang & Jing Chen

  2. School of Information Technology, Beijing Institute of Technology Zhuhai, Zhuhai, China

    Kejun Wang

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  1. Chunyan Lyu
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Correspondence to Kejun Wang.

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Lyu, C., Xu, T., Wang, K. et al. Person re-identification based on human semantic parsing and message passing. J Supercomput 79, 5223–5247 (2023). https://doi.org/10.1007/s11227-022-04866-w

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