A person re-identification network based on multi-granularity feature extraction

Pu Yan Sr.; Qingwei Tang Sr.; Jie Chen Sr.

doi:10.1117/12.2636850

29 April 2022 A person re-identification network based on multi-granularity feature extraction

Pu Yan Sr., Qingwei Tang Sr., Jie Chen Sr.

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 122471Q (2022) https://doi.org/10.1117/12.2636850
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

The purpose of person re-identification (Re-ID) is to retrieve a person of interest from a set of images taken by multiple cameras. In some current work, simple global features and local features do not allow the model to achieve excellent performance. In this paper, we propose an end-to-end person re-identification network that integrates multi-granularity pedestrian features. Our model contains multiple branching feature extraction modules, specifically, two global feature extraction modules, two auxiliary modules and two attention modules. To enhance the feature extraction capability of the model, we embed an improved parameter-free attention module in the backbone network, which significantly improves the performance. Our comprehensive experiments on the mainstream evaluation datasets of Market-1501, DukeMTMCreid show that our method achieves a more advanced performance that outperforms most existing methods. As an example, on the Market-1501 dataset, with the help of re-ranking(RK) strategy, we get the result of rank-1/mAP=95.8%/94.0% which exceeds most current methods.

Citation Download Citation

Pu Yan Sr., Qingwei Tang Sr., and Jie Chen Sr. "A person re-identification network based on multi-granularity feature extraction", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 122471Q (29 April 2022); https://doi.org/10.1117/12.2636850

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